江南大学的丁锋及合作者靠大量中低水平论文和自引获得荣耀

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江南大学的丁锋及合作者靠大量中低水平论文和自引获得荣耀

作者：杜克林

1、前言

11月6日，我收到Springer发来的电子邮件，推荐阅读Circuits, Systems, and Signal Processing期刊的三篇最近出版的高引用率论文。自从2009年以来，我一直是该期刊的编委/副编辑(Associate Editor)，我想研究一下这三篇论文。

这三篇论文是：

1. Ling Xu, Feng Ding. Iterative Parameter Estimation for Signal Models Based on Measured Data. Circuits, Systems, and Signal Processing, July 2018, Volume 37, Issue 7, pp 3046–3069 　　2. Jiling Ding, Recursive and Iterative Least Squares Parameter Estimation Algorithms for Multiple-Input–Output-Error Systems with Autoregressive Noise. Circuits, Systems, and Signal Processing, May 2018, Volume 37, Issue 5, pp 1884–1906. 　　3. Junhong Li, Wei Xing Zheng, Juping Gu, Liang Hua. A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem. Circuits, Systems, and Signal Processing, June 2018, Volume 37, Issue 6, pp 2374–2393.

首先，这三篇论文的题目都是非常相似的，都是关于参数估计和辨识的。这些论文从标题就看得出是传统的课题，看似解决了什么问题，又好像什么也没解决。信号处理领域研究方向和热点非常多，为什么这三篇论文成为高引用论文，我决定挖一挖。我将把调查结果向Circuits, Systems, and Signal Processing期刊的编委会汇报。

2、第一篇论文的引用和被引用分析

第一篇作者Ling Xu, Feng Ding来自于江南大学物联网学院。Ling Xu还属于无锡商业职业技术学院物联网技术学院。

First Online: 11 November 2017　　348 Downloads, 52 Citations　　58 References

（一） 参考文献（References）分析

该文的参考文献如下：

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18. Y. Ji, F. Ding, Multiperiodicity and exponential attractivity of neural networks with mixed delays. Circuits Syst Signal Process. 36(6), 2558–2573 (2017)

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21. M.H. Li, X.M. Liu et al., The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive noise. J. Franklin Inst. 354(12), 4861–4881 (2017)

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29. W. Liu, State estimation for discrete-time Markov jump linear systems with time-correlated measurement noise. Automatica 76, 266–276 (2017)

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31. Y.W. Mao, F. Ding, A novel parameter separation based identification algorithm for Hammerstein systems. Appl. Math. Lett. 60, 21–27 (2016)

32. J. Pan, X. Jiang, X.K. Wan, W. Ding, A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems. Int. J. Control Autom. Syst. 15(3), 1189–1197 (2017)

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39. D.Q. Wang, Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models. Appl. Math. Lett. 57, 13–19 (2016)

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41. X.H. Wang, F. Ding, Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems. Int. J. Adapt. Control Signal Process. 30(6), 824–842 (2016)

42. Y.J. Wang, F. Ding, L. Xu, Some new results of designing an IIR filter with colored noise for signal processing. Digital Signal Process. 72, 44–58 (2018)

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57. L. Xu, F. Ding, The parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle. IET Signal Process. 11(2), 228–237 (2017)

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在该文的58篇参考文献中，有20篇论文（[6，9-12，18，30，31， 40-42，49-57]）是作者自己（Ling Xu, Feng Ding）的论文。另有2篇（26，28）他们的有关联的合作者 Wen Xing Zheng论文。

可见作者的自引率是相当高的。

（二） 引用（Citations）分析。

引用该文的文献如下：

1. Jing Yan, Xuyang Tian, Xiaoyuan Luo and Xinping Guan. Design of an Embedded Communication System for Underwater Asynchronous Localization. IEEE Embedded Systems Letters, 2019, Volume 11, Number 3, Page 97. [0 CITATIONS]

2. Xiao Zhang, Feng Ding, Ling Xu, Ahmed Alsaedi and Tasawar Hayat. A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise, Mathematics, 2019, Volume 7, Number 4, Page 356. [11 CITATIONS]

3. Ping Ma, Feng Ding and Tasawar Hayat. Multi-innovation gradient estimation algorithms for multivariate equation-error autoregressive moving average systems based on the filtering technique, IET Control Theory & Applications, 2019, Volume 13, Number 13, Page 2086. [0 CITATIONS]

4. Huafeng Xia, Yan Ji, Yanjun Liu and Ling Xu. Maximum Likelihood-based Multi-innovation Stochastic Gradient Method for Multivariable Systems, International Journal of Control, Automation and Systems, 2019, Volume 17, Number 3, Page 565. [1 CITATIONS]

5. Xuehai Wang and Feng Ding. The filtering based parameter identification for bilinear-in-parameter systems, Journal of the Franklin Institute, 2019, Volume 356, Number 1, Page 514. [1 CITATIONS]

6. Jing Chen, Feng Ding, Quanmin Zhu and Yanjun Liu. Maximum likelihood based identification methods for rational models, International Journal of Systems Science, 2019, Volume 50, Number 14, Page 2579. [0 CITATIONS]

7. Xiao Zhang, Feng Ding, Ling Xu and Erfu Yang. Highly computationally efficient state filter based on the delta operator. International Journal of Adaptive Control and Signal Processing, 2019, Volume 33, Number 6, Page 875. [15 CITATIONS]

8. Feng Ding, Jian Pan, Ahmed Alsaedi and Tasawar Hayat, Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data. Mathematics, 2019, Volume 7, Number 5, Page 428. [11 CITATIONS]

9. Huan Xu, Feng Ding and Erfu Yang. Recursive search-based identification algorithms for the exponential autoregressive time series model with coloured noise. IET Control Theory & Applications, 2019, DOI: 10.1049/iet-cta.2019.0429 [0 CITATIONS]

10. Lijuan Wan and Feng Ding. Decomposition- and Gradient-Based Iterative Identification Algorithms for Multivariable Systems Using the Multi-innovation Theory, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 7, Page 2971. [14 CITATIONS]

11. Lijuan Liu, Feng Ding, Ling Xu, Jian Pan, Ahmed Alsaedi and Tasawar Hayat. Maximum Likelihood Recursive Identification for the Multivariate Equation-Error Autoregressive Moving Average Systems Using the Data Filtering, IEEE Access, 2019, Volume 7, Page 41154. [1 CITATIONS].

12. Qinyao Liu and Feng Ding. Auxiliary Model-Based Recursive Generalized Least Squares Algorithm for Multivariate Output-Error Autoregressive Systems Using the Data Filtering. Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 2, Page 590. [22 CITATIONS].

13. Ting Cui, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Recursive parameter and state estimation methods for observability canonical state-space models exploiting the hierarchical identification principle, IET Control Theory & Applications, 2019, Volume 13, Number 16, Page 2538. [0 CITATIONS]

14. Huan Xu, Lijuan Wan, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Fitting the exponential autoregressive model through recursive search, Journal of the Franklin Institute, 2019, Volume 356, Number 11, Page 5801. [1 CITATIONS]

15. Hao Ma, Jian Pan, Lei Lv, Guanghui Xu, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Recursive Algorithms for Multivariable Output-Error-Like ARMA Systems, Mathematics, 2019, Volume 7, Number 6, Page 558. [5 CITATIONS]

16. Ling Xu, Feng Ding and Quanmin Zhu, Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses. International Journal of Systems Science, 2019, Volume 50, Number 1, Page 141. [22 CITATIONS]

17. Lijuan Liu, Feng Ding and Quanmin Zhu. Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise. International Journal of Systems Science, 2018, Volume 49, Number 13, Page 2763. [2 CITATIONS]

18. Bingbing Shen, Feng Ding, Ling Xu and Tasawar Hayat. Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 5, Page 2225. [1 CITATIONS]

19. Shoupeng Song and Jingjing Shen. Exponential-Reproducing-Kernel-Based Sparse Sampling Method for Finite Rate of Innovation Signal with Arbitrary Pulse Echo Position, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 3, Page 1179. [0 CITATIONS]

20. Yuanbiao Hu, Qin Zhou, Hao Yu, Zheng Zhou and Feng Ding. Two-Stage Generalized Projection Identification Algorithms for Stochastic Systems, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 6, Page 2846. [2 CITATIONS]

21. Lijuan Wan, Ximei Liu, Feng Ding and Chunping Chen. Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems, Mathematics, 2019, Volume 7, Number 7, Page 609. [2 CITATIONS]

22. Junxia Ma, Qiulin Fei and Weili Xiong. Sliding Window Iterative Identification of Systems With Asymmetric Preload Nonlinearity Based on the Key Term Separation, IEEE Access, 2019, Volume 7, Page 36633. [0 CITATIONS]

23. Huafeng Xia, Yongqing Yang, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique, International Journal of Systems Science, 2019, Volume 50, Number 6, Page 1121. [0 CITATIONS]

24. Mengting Chen, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Iterative Identification Algorithms for Bilinear-in-parameter Systems by Using the Over-parameterization Model and the Decomposition, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 6, Page 2634. [2 CITATIONS]

25. Jian Pan, Wei Li and Haipeng Zhang. Control Algorithms of Magnetic Suspension Systems Based on the Improved Double Exponential Reaching Law of Sliding Mode Control, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 6, Page 2878. [38 CITATIONS]

26. Guang-Yong Chen, Min Gan, C. L. Philip Chen and Long Chen. A Two-Stage Estimation Algorithm Based on Variable Projection Method for GPS Positioning, IEEE Transactions on Instrumentation and Measurement, 2018, Volume 67, Number 11, Page 2518. [3 CITATIONS]

27. Ling Xu, Feng Ding and Feng Ding. Algebraic parameter estimation approaches for process control systems from sine responses, 2019 Chinese Control And Decision Conference (CCDC), Year: 2019, Page 1720. [0 CITATIONS]

28. Huan Xu, Feng Ding and Jie Sheng. On some parameter estimation algorithms for the nonlinear exponential autoregressive model, International Journal of Adaptive Control and Signal Processing, 2019, Volume 33, Number 6, Page 999. [1 CITATIONS]

29. Xiao Zhang, Feng Ding and Erfu Yang, State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors. International Journal of Adaptive Control and Signal Processing, 2019, Volume 33, Number 7, Page 1157. [7 CITATIONS]

30. Yasser Shekofteh, Sajad Jafari, Karthikeyan Rajagopal and Viet-Thanh Pham. Parameter Identification of Chaotic Systems Using a Modified Cost Function Including Static and Dynamic Information of Attractors in the State Space, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 5, Page 2039. [1 CITATIONS]

31. Cheng Wang and Kaicheng Li, Aitken-Based Stochastic Gradient Algorithm for ARX Models with Time Delay. Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 6, Page 2863.. [0 CITATIONS]

32. Ling Xu, Weili Xiong, Ahmed Alsaedi and Tasawar Hayat. Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 4, Page 1756. [46 CITATIONS]

33. Qinyao Liu, Feng Ding and Erfu Yang. Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems, Digital Signal Processing, 2018, Volume 83, Page 323. [4 CITATIONS]

34. Qinyao Liu, Feng Ding, Ling Xu and Erfu Yang. Partially coupled gradient estimation algorithm for multivariable equation-error autoregressive moving average systems using the data filtering technique, IET Control Theory & Applications, 2019, Volume 13, Number 5, Page 642. [11 CITATIONS]

35. Huafeng Xia, Yongqing Yang, Feng Ding, Ling Xu and Tasawar Hayat. Maximum likelihood gradient-based iterative estimation for multivariable systems, IET Control Theory & Applications, 2019, Volume 13, Number 11, Page 1683. [0 CITATIONS]

36. Ya Gu, Jicheng Liu, Xiangli Li, Yongxin Chou and Yan Ji, State space model identification of multirate processes with time-delay using the expectation maximization. Journal of the Franklin Institute, 2019, Volume 356, Number 3, Page 1623. [14 CITATIONS]

37. Zhengwei Ge, Feng Ding, Ling Xu, Ahmed Alsaedi and Tasawar Hayat. Gradient-based iterative identification method for multivariate equation-error autoregressive moving average systems using the decomposition technique, Journal of the Franklin Institute, 2019, Volume 356, Number 3, Page 1658. [17 CITATIONS]

38. Ya Gu, Yongxin Chou, Jicheng Liu and Yan Ji. Moving horizon estimation for multirate systems with time-varying time-delays, Journal of the Franklin Institute, 2019, Volume 356, Number 4, Page 2325. [15 CITATIONS]

39. Ting Cui, Feng Ding, Xiangli Li and Tasawar Hayat. Kalman filtering based gradient estimation algorithms for observer canonical state-space systems with moving average noises, Journal of the Franklin Institute, 2019, Volume 356, Number 10, Page 5485. [0 CITATIONS]

40. Jian Pan, Hao Ma, Xiao Jiang, Wenfang Ding and Feng Ding. Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique, Complexity, 2018, Volume 2018, Page 1. [30 CITATIONS]

41. Lijuan Liu, Feng Ding, Cheng Wang, Ahmed Alsaedi and Tasawar Hayat. Maximum Likelihood Multi-innovation Stochastic Gradient Estimation for Multivariate Equation-error Systems, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 5, Page 2528. [0 CITATIONS]

42. Yunze Guo, Lijuan Wan, Ling Xu, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Two-stage Recursive Least Squares Parameter Estimation Algorithm for Multivariate Output-error Autoregressive Moving Average Systems, International Journal of Control, Automation and Systems, 2019, Volume 17, Number 6, Page 1547. [0 CITATIONS]

43. Siyu Liu, Feng Ding, Ling Xu and Tasawar Hayat. Hierarchical Principle-Based Iterative Parameter Estimation Algorithm for Dual-Frequency Signals, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 7, Page 3251. [11 CITATIONS]

44. Jie Ding, Jiazhong Chen, Jinxing Lin and Lijuan Wan. Particle filtering based parameter estimation for systems with output-error type model structures, Journal of the Franklin Institute, 2019, Volume 356, Number 10, Page 5521. [11 CITATIONS]

45. Mengting Chen and Feng Ding. Iterative Identification of Discrete-Time Systems With Bilinear Forms in the Presence of Colored Noises Based on the Hierarchical Principle, Journal of Computational and Nonlinear Dynamics, 2019, Volume 14, Number 9. [0 CITATIONS]

46. Mengting Chen, Feng Ding, Rongming Lin, Ahmed Alsaedi and Tasawar Hayat. Parameter estimation for a special class of nonlinear systems by using the over-parameterisation method and the linear filter, International Journal of Systems Science, 2019, Volume 50, Number 9, Page 1689. [0 CITATIONS]

47. Longjin Wang, Yan Ji, Lijuan Wan and Ni Bu. Hierarchical recursive generalized extended least squares estimation algorithms for a class of nonlinear stochastic systems with colored noise, Journal of the Franklin Institute, 2019, Volume 356, Number 16, Page 10102. [0 CITATIONS]

48. Mengting Chen, Feng Ding and Erfu Yang. Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique, IET Control Theory & Applications, 2018, Volume 12, Number 17, Page 2380. [2 CITATIONS]

49. Ling Xu, Feng Ding and Feng Ding. Proceedings of 2019 Chinese Intelligent Systems Conference. Series: Lecture Notes in Electrical Engineering, Year: 2020, Volume 594, Page 620. [0 CITATIONS]

50. Huan Xu, Feng Ding and Erfu Yang. Modeling a nonlinear process using the exponential autoregressive time series model, Nonlinear Dynamics, 2019, Volume 95, Number 3, Page 2079. [16 CITATIONS]

51. Xian Lu, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Decomposition-based Gradient Estimation Algorithms for Multivariable Equation-error Systems, International Journal of Control, Automation and Systems, 2019, Volume 17, Number 8, Page 2037. [0 CITATIONS]

52. Huafeng Xia, Yan Ji, Ling Xu and Tasawar Hayat. Maximum Likelihood-Based Recursive Least-Squares Algorithm for Multivariable Systems with Colored Noises Using the Decomposition Technique, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 3, Page 986. [2 CITATIONS]

在所有引用这篇论文的58篇论文中，有41篇（[2]-[18], [20]-[21], [23]-[24], [27]-[29], [32]-[35], [37], [39]-[43], [45]-[46], [48]-[52] ）是作者Ling Xu, Feng Ding的论文。另外10篇也是中国大陆作者，只有一篇是非华裔作者的论文。如果大家再挖掘一下那10篇论文，主要挖掘作者的单位，以及那些作者有没有与这两位作者共同发表过论文，以及与这两位作者的合作者共同发表过论文，肯定还会有新的发现。

3、第二篇论文的引用和被引用分析

作者Jiling Ding也来自于江南大学物联网学院。　　First Online: 31 August 2017　　345 Downloads, 42 Citations　　48 References

（一） 参考文献（References）分析

该文的参考文献如下：

1. A. Cristofaro, S. Pettinari, Fault accommodation for multi-input linear sampled-data systems. Int. J. Adapt. Control Signal Process. 29, 835–854 (2015)

2. F. Ding, X.H. Wang, L. Mao, L. Xu, Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering. Digital Signal Process. 62, 211–223 (2017)

3. F. Ding, F.F. Wang, L. Xu, T. Hayat, A. Alsaedi, Parameter estimation for pseudo-linear systems using the auxiliary model and the decomposition technique. IET Control Theory Appl. 11(3), 390–400 (2017)

4. F. Ding, F.F. Wang, L. Xu, M.H. Wu, Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering. J. Frankl. Inst. 354(3), 1321–1339 (2017)

5. F. Ding, L. Xu, Q.M. Zhu, Performance analysis of the generalised projection identification for time-varying systems. IET Control Theory Appl. 10(18), 2506–02514 (2016)

6. L. Feng, M.H. Wu, Q.X. Li et al., Array factor forming for image reconstruction of one-dimensional nonuniform aperture synthesis radiometers. IEEE Geosci. Remote Sens. Lett. 13(2), 237–241 (2016)

7. G.X. Gu, S. Wan, L. Qiu, Networked stabilization for multi-input systems over quantized fading channels. Automatica 61, 1–8 (2015)

8. Y. Ji, F. Ding, Multiperiodicity and exponential attractivity of neural networks with mixed delays. Circuits Syst. Signal Process. 36(6), 2558–2573 (2017)

9. Y. Ji, X.M. Liu, Unified synchronization criteria for hybrid switching-impulsive dynamical networks. Circuits Syst. Signal Process. 34(5), 1499–1517 (2015)

10. M.H. Li, X.M. Liu, F. Ding, Least-squares-based iterative and gradient-based iterative estimation algorithms for bilinear systems. Nonlinear Dyn. 89(1), 197–211 (2017)

11. M.H. Li, X.M. Liu, F. Ding, The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive noise. J. Frankl. Inst. 354(12), 4861–4881 (2017)

12. M.H. Li, X.M. Liu, F. Ding, The gradient based iterative estimation algorithms for bilinear systems with autoregressive noise. Circuits Syst. Signal Process. (2017). doi: 10.1007/s00034-017-0527-4

13. H. Li, Y. Shi, W. Yan, Distributed receding horizon control of constrained nonlinear vehicle formations with guaranteed gamma-gain stability. Automatica 68, 148–154 (2016)

14. H. Li, Y. Shi, W. Yan, On neighbor information utilization in distributed receding horizon control for consensus-seeking. IEEE Trans. Cybern. 46(9), 2019–2027 (2016)

15. H. Li, W.S. Yan, Y. Shi, Continuous-time model predictive control of under-actuated spacecraft with bounded control torques. Automatica 75, 144–153 (2016)

16. J.H. Li, W.X. Zheng, J.P. Gu, L. Hua, Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements. J. Frankl. Inst. 354(1), 316–331 (2017)

17. X.G. Liu, J. Lu, Least squares based iterative identification for a class of multirate systems. Automatica 46(3), 549–554 (2010)

18. Y.W. Mao, F. Ding, Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive systems based on the filtering technique. Nonlinear Dyn. 79(3), 1745–1755 (2015)

19. D.D. Meng, Recursive least squares and multi-innovation gradient estimation algorithms for bilinear stochastic systems. Circuits Syst. Signal Process. 35(3), 1052–1065 (2016)

20. G. Mercère, L. Bako, Parameterization and identification of multivariable state-space systems: a canonical approach. Automatica 47(8), 1547–1555 (2011)

21. S. Mobayen, An LMI-based robust tracker for uncertain linear systems with multiple time-varying delays using optimal composite nonlinear feedback technique. Nonlinear Dyn. 80, 917–927 (2015)

22. J. Na, G. Herrmann, K.Q. Zhang, Improving transient performance of adaptive control via a modified reference model and novel adaptation. Int. J. Robust Nonlinear Control 27(8), 1351–1372 (2017)

23. J. Na, M.N. Mahyuddin, G. Herrmann et al., Robust adaptive finite-time parameter estimation and control for robotic systems. Int. J. Robust Nonlinear Control 25(16), 3045–3071 (2015)

24. J. Na, J. Yang, X.M. Ren et al., Robust adaptive estimation of nonlinear system with time-varying parameters. Int. J. Adapt. Control Signal Process. 29(8), 1055–1072 (2015)

25. J. Na, J. Yang, X. Wu et al., Robust adaptive parameter estimation of sinusoidal signals. Automatica 53, 376–384 (2015)

26. A. Nasirin, S.K. Nguang, A. Swain, Adaptive sliding mode control for a class of MIMO nonlinear systems with uncertainties. J. Frankl. Inst. 351(4), 2048–2061 (2014)

27. J. Pan, X. Jiang, X.K. Wan, W.F. Ding, A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems. Int. J. Control Autom. Syst. 15(3), 1189–1197 (2017)

28. J. Pan, X.H. Yang, H.F. Cai, B.X. Mu, Image noise smoothing using a modified Kalman filter. Neurocomputing 173, 1625–1629 (2016)

29. X.K. Wan, Y. Li, C. Xia, M.H. Wu, J. Liang, N. Wang, A T-wave alternans assessment method based on least squares curve fitting technique. Measurement 86, 93–100 (2016)

30. Y.J. Wang, F. Ding, Recursive parameter estimation algorithms and convergence for a class of nonlinear systems with colored noise. Circuits Syst. Signal Process. 35(10), 3461–3481 (2016)

31. Y.J. Wang, F. Ding, Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering. Nonlinear Dyn. 84(2), 1045–1053 (2016)

32. Y.J. Wang, F. Ding, Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model. Automatica 71, 308–313 (2016)

33.Y.J. Wang, F. Ding, The filtering based iterative identification for multivariable systems. IET Control Theory Appl. 10(8), 894–902 (2016)

34. X.H. Wang, F. Ding, Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems. Int. J. Adapt. Control Signal Process. 30(6), 824–842 (2016)

35. X.H. Wang, F. Ding, Joint estimation of states and parameters for an input nonlinear state-space system with colored noise using the filtering technique. Circuits Syst. Signal Process. 35(2), 481–500 (2016)

36. X.H. Wang, F. Ding, Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle. Signal Process. 117, 208–218 (2015)

37. D.Q. Wang, Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models. Appl. Math. Lett. 57, 13–19 (2016)

38. D.Q. Wang, L. Mao, F. Ding, Recasted models based hierarchical extended stochastic gradient method for MIMO nonlinear systems. IET Control Theory Appl. 11(4), 476–485 (2017)

39. H.N. Wu, J.W. Wang, Observer design and output feedback stabilization for nonlinear multivariable systems with diffusion PDE-governed sensor dynamics. Nonlinear Dyn. 72, 615–628 (2013)

40. L. Xu, A proportional differential control method for a time-delay system using the Taylor expansion approximation. Appl. Math. Comput. 236, 391–399 (2014)

41. L. Xu, Application of the Newton iteration algorithm to the parameter estimation for dynamical systems. J. Comput. Appl. Math. 288, 33–43 (2015)

42. L. Xu, The damping iterative parameter identification method for dynamical systems based on the sine signal measurement. Signal Process. 120, 660–667 (2016)

43. L. Xu, The parameter estimation algorithms based on the dynamical response measurement data. Adv. Mech. Eng. 9(9) (2017).

44. L. Xu, L. Chen, W.L. Xiong, Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration. Nonlinear Dyn. 79(3), 2155–2163 (2015)

45. L. Xu, F. Ding, The parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle. IET Signal Process. 11(2), 228–237 (2017)

46. L. Xu, F. Ding, Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling. Circuits Syst. Signal Process. 36(4), 1735–1753 (2017)

47. L. Xu, F. Ding, Y. Gu, A. Alsaedi, T. Hayat, A multi-innovation state and parameter estimation algorithm for a state space system with d-step state-delay. Signal Process. 140, 97–103 (2017)

48. N. Zhao, M.H. Wu, J.J. Chen, Android-based mobile educational platform for speech signal processing. Int. J. Electr. Eng. Educ. 54(1), 3–16 (2017)

在48篇参考文献中，有28篇是 （2-5，8-12,18, 27, 30-38, 40-47）是 Feng Ding, Ling Xu, X.H. Wang, D.Q. Wang, Wenfang Ding 的论文。他们是同一个小组共同发表了论文。在这47篇参考文献中，只有4篇是非中国大陆作者的论文。

（二） 引用（Citations）分析。

引用该文的文献如下：

1. Jie Ding, Jiazhong Chen, Jinxing Lin and Lijuan Wan. Particle filtering based parameter estimation for systems with output-error type model structures, Journal of the Franklin Institute, 2019, Volume 356, Number 10, Page 5521. [11 CITATIONS]

2. Mengting Chen, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Iterative Identification Algorithms for Bilinear-in-parameter Systems by Using the Over-parameterization Model and the Decomposition, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 6, Page 2634. [2 CITATIONS]

3. Jiling Ding and Weihai Zhang. Recursive least squares based hierarchical estimation for multi-input nonlinear systems, Conference: 2019 Chinese Control And Decision Conference (CCDC), Year: 2019, Page 4056. [0 CITATIONS]

4. Huan Xu, Feng Ding and Jie Sheng. On some parameter estimation algorithms for the nonlinear exponential autoregressive model, International Journal of Adaptive Control and Signal Processing, 2019, Volume 33, Number 6, Page 999. [1 CITATIONS]

5. Meihang Li, Ximei Liu and Feng Ding. The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle, International Journal of Adaptive Control and Signal Processing, 2019, Volume 33, Number 7, Page 1189. [5 CITATIONS]

6. Feng Ding, Xiao Zhang and Ling Xu. The innovation algorithms for multivariable state‐space models, International Journal of Adaptive Control and Signal Processing, 2019, Volume 33, Number 11, Page 1601. [0 CITATIONS]

7. Feng Ding, Dandan Meng, Jiyang Dai, Qishen Li, Ahmed Alsaedi and Tasawar Hayat. Least Squares based Iterative Parameter Estimation Algorithm for Stochastic Dynamical Systems with ARMA Noise Using the Model Equivalence, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 2, Page 630. [28 CITATIONS]]

8. Cheng Wang and Kaicheng Li. Aitken-Based Stochastic Gradient Algorithm for ARX Models with Time Delay, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 6, Page 2863. [0 CITATIONS]

9. Feng Ding, Huibo Chen, Ling Xu, Jiyang Dai, Qishen Li and Tasawar Hayat. A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation, Journal of the Franklin Institute, 2018, Volume 355, Number 8, Page 3737. [50 CITATIONS]

10. Ling Xu, Weili Xiong, Ahmed Alsaedi and Tasawar Hayat. Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 4, Page 1756. [46 CITATIONS]

11. Qinyao Liu, Feng Ding and Erfu Yang. Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems, Digital Signal Processing, 2018, Volume 83, Page 323. [4 CITATIONS]

12. Qinyao Liu, Feng Ding, Ling Xu and Erfu Yang. Partially coupled gradient estimation algorithm for multivariable equation-error autoregressive moving average systems using the data filtering technique, IET Control Theory & Applications, 2019, Volume 13, Number 5, Page 642. [11 CITATIONS]

13. ShuMing He and Yun Lin. Cauchy Distribution Function-Penalized LMS for Sparse System Identification, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 1, Page 470. [0 CITATIONS]

14. Jian Pan, Hao Ma, Xiao Jiang, Wenfang Ding and Feng Ding, Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique, Complexity, 2018, Volume 2018, Page 1. [30 CITATIONS]

15. Mengting Chen, Feng Ding, Rongming Lin, Ahmed Alsaedi and Tasawar Hayat. Parameter estimation for a special class of nonlinear systems by using the over-parameterisation method and the linear filter, Journal: International Journal of Systems Science, 2019, Volume 50, Number 9, Page 1689. [0 CITATIONS]

16. Lijuan Liu, Feng Ding, Cheng Wang, Ahmed Alsaedi and Tasawar Hayat. Maximum Likelihood Multi-innovation Stochastic Gradient Estimation for Multivariate Equation-error Systems, Journal: International Journal of Control, Automation and Systems, 2018, Volume 16, Number 5, Page 2528. [0 CITATIONS]

17. Lijuan Liu, Yan Wang, Cheng Wang, Feng Ding and Tasawar Hayat. Maximum likelihood recursive least squares estimation for multivariate equation-error ARMA systems, Journal of the Franklin Institute, 2018, Volume 355, Number 15, Page 7609. [2 CITATIONS]

18. Qinyao Liu, Feng Ding, Yan Wang, Cheng Wang and Tasawar Hayat. Auxiliary model based recursive generalized least squares identification algorithm for multivariate output-error autoregressive systems using the decomposition technique, Journal of the Franklin Institute, 2018, Volume 355, Number 15, Page 7643. [1 CITATIONS]

19. Yunze Guo, Lijuan Wan, Ling Xu, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Two-stage Recursive Least Squares Parameter Estimation Algorithm for Multivariate Output-error Autoregressive Moving Average Systems, International Journal of Control, Automation and Systems, 2019, Volume 17, Number 6, Page 1547. [0 CITATIONS]

20. Siyu Liu, Feng Ding, Ling Xu and Tasawar Hayat. Hierarchical Principle-Based Iterative Parameter Estimation Algorithm for Dual-Frequency Signals, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 7, Page 3251. [11 CITATIONS]

21. Mengting Chen, Feng Ding and Erfu Yang. Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique, IET Control Theory & Applications, 2018, Volume 12, Number 17, Page 2380. [2 CITATIONS]

22. Lijuan Wan, Ximei Liu, Feng Ding and Chunping Chen. Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems, Mathematics, 2019, Volume 7, Number 7, Page 609. [2 CITATIONS]

23. Peiman Davari Dolatabadi, Karen Khanlari, Mohsen Ghafory Ashtiany and Mahmood Hosseini. System identification method by using inverse solution of equations of motion in time domain and noisy condition, Physica A: Statistical Mechanics and its Applications, 2020, Volume 538, Page 122680. [0 CITATIONS]

24. Xian Lu, Feng Ding, Ahmed Alsaedi and Tasawar Hayat, Decomposition-based Gradient Estimation Algorithms for Multivariable Equation-error Systems. International Journal of Control, Automation and Systems, 2019, Volume 17, Number 8, Page 2037. [0 CITATIONS]

25. Huafeng Xia, Yan Ji, Ling Xu and Tasawar Hayat. Maximum Likelihood-Based Recursive Least-Squares Algorithm for Multivariable Systems with Colored Noises Using the Decomposition Technique, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 3, Page 986. [2 CITATIONS]

26. Cheng Wang, Kaicheng Li and Shuai Su. Hierarchical Newton Iterative Parameter Estimation of a Class of Input Nonlinear Systems Based on the Key Term Separation Principle, Complexity, 2018, Volume 2018, Page 1. [1 CITATIONS]

27. Meihang Li, Ximei Liu and Feng Ding. Filtering-Based Maximum Likelihood Gradient Iterative Estimation Algorithm for Bilinear Systems with Autoregressive Moving Average Noise, Journal: Circuits, Systems, and Signal Processing, 2018, Volume 37, Number 11, Page 5023. [9 CITATIONS].

28. Huafeng Xia, Yan Ji, Yanjun Liu and Ling Xu. Maximum Likelihood-based Multi-innovation Stochastic Gradient Method for Multivariable Systems, International Journal of Control, Automation and Systems, 2019, Volume 17, Number 3, Page 565. [1 CITATIONS]

29. Feiyan Chen, Feng Ding, Ling Xu and Tasawar Hayat. Data filtering based maximum likelihood extended gradient method for multivariable systems with autoregressive moving average noise, Journal of the Franklin Institute, 2018, Volume 355, Number 7, Page 3381. [2 CITATIONS]

30. Ling Xu, Feng Ding and Quanmin Zhu. Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses, International Journal of Systems Science, 2019, Volume 50, Number 1, Page 141. [22 CITATIONS]

31. Rajalakshmi Murugesan, Jeyadevi Solaimalai and Karthik Chandran. Computer-Aided Controller Design for a Nonlinear Process Using a Lagrangian-Based State Transition Algorithm, Circuits, Systems, and Signal Processing, 2019. [0 CITATIONS]

32. Qinyao Liu and Feng Ding. Auxiliary Model-Based Recursive Generalized Least Squares Algorithm for Multivariate Output-Error Autoregressive Systems Using the Data Filtering, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 2, Page 590. [22 CITATIONS]

33. Xiao Zhang, Feng Ding, Ling Xu and Erfu Yang. State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle, IET Control Theory & Applications, 2018, Volume 12, Number 12, Page 1704. [53 CITATIONS]

34. Ting Cui, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Recursive parameter and state estimation methods for observability canonical state-space models exploiting the hierarchical identification principle, IET Control Theory & Applications, 2019, Volume 13, Number 16, Page 2538. [0 CITATIONS]

35. Lijuan Liu, Feng Ding and Quanmin Zhu. Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise, International Journal of Systems Science, 2018, Volume 49, Number 13, Page 2763. [2 CITATIONS]

36. Ya Gu, Jicheng Liu, Xiangli Li, Yongxin Chou and Yan Ji. State space model identification of multirate processes with time-delay using the expectation maximization, Journal of the Franklin Institute, 2019, Volume 356, Number 3, Page 1623. [14 CITATIONS]

37. Zhengwei Ge, Feng Ding, Ling Xu, Ahmed Alsaedi and Tasawar Hayat. Gradient-based iterative identification method for multivariate equation-error autoregressive moving average systems using the decomposition technique, Journal of the Franklin Institute, 2019, Volume 356, Number 3, Page 1658. [17 CITATIONS]

38. Ting Cui, Feng Ding, Xiangli Li and Tasawar Hayat. Kalman filtering based gradient estimation algorithms for observer canonical state-space systems with moving average noises. Journal of the Franklin Institute, 2019, Volume 356, Number 10, Page 5485. [0 CITATIONS]

39. Bingbing Shen, Feng Ding, Ling Xu and Tasawar Hayat. Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 5, Page 2225. [1 CITATIONS]

40. Yuanbiao Hu, Qin Zhou, Hao Yu, Zheng Zhou and Feng Ding. Two-Stage Generalized Projection Identification Algorithms for Stochastic Systems, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 6, Page 2846. [2 CITATIONS]

41. Junhong Li and Xiao Li, Particle Swarm Optimization Iterative Identification Algorithm and Gradient Iterative Identification Algorithm for Wiener Systems with Colored Noise. Complexity, 2018, Volume 2018, Page 1. [1 CITATIONS]

42. Huafeng Xia, Yongqing Yang, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique, International Journal of Systems Science, 2019, Volume 50, Number 6, Page 1121. [0 CITATIONS]

在总共42次引用中，有35次引用（[2], [3]-[7], [9]-[12], [14]-[22], [24]-[25], [27]-[30], [32]-[35], [37]-[42]）是由Feng Ding,Jiling Ding, Ling Xu, Junhong Li等合作发表的人引用的。除了1次引用来自非华裔，其他引用全部来自中国大陆作者。

4、第三篇论文的引用和被引用分析

作者 Junhong Li, Wei Xing Zheng,Juping Gu, Liang Hua 大多数来自于南通大学电气工程学院。Wei Xing Zheng的单位是 School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, Australia。

First Online: 20 October 2017　　369 Downloads, 21 Citations　　38 References

（一） 参考文献（References）分析

该文的参考文献如下：

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2. F. Ding, Combined state and least squares parameter estimation algorithms for dynamic systems. Appl. Math. Model. 38(1), 403–412 (2014)

3. F. Ding, L. Xu, Q.M. Zhu, Performance analysis of the generalised projection identification for time-varying systems. IET Control Theory Appl. 10(18), 2506–2514 (2016)

4. F. Ding, X.H. Wang, L. Mao, L. Xu, Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering. Digit. Signal Process. 62, 211–223 (2017)

5. F. Ding, F.F. Wang, L. Xu, M.H. Wu, Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering. J. Frankl. Inst. 354(3), 1321–1339 (2017)

6. F. Ding, F.F. Wang, T. Hayat, A. Alsaedi, Parameter estimation for pseudo-linear systems using the auxiliary model and the decomposition technique. IET Control Theory Appl. 11(3), 390–400 (2017)

7. W. Favoreel, B. De Moor, P.V. Overschee, Subspace state space system identification for industrial processes. J. Process Control 10(2–3), 149–155 (2000)

8. J.C. Gómez, E. Baeyens, Identification of block-oriented nonlinear systems using orthonormal bases. J. Process Control 14(6), 685–697 (2004)

9. W. Greblicki, Nonparametric approach to Wiener system identification. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 44(6), 538–545 (1997)

10. Y. Gu, X. Lu, R. Ding, Parameter and state estimation algorithm for a state space model with a one-unit state delay. Circuits Syst. Signal Process. 32(5), 2267–2280 (2013)

11. J. Guo, L.Y. Wang, G. Yin, Y.L. Zhao, J.F. Zhang, Identification of Wiener systems with quantized inputs and binary-valued output observations. Automatica 78, 280–286 (2017)

12. G. Li, C. Wen, W.X. Zheng, Y. Chen, Identification of a class of nonlinear autoregressive models with exogenous inputs based on kernel machines. IEEE Trans. Signal Process. 59(5), 2146–2159 (2011)

13. J.H. Li, F. Ding, G.W. Yang, Maximum likelihood least squares identification method for input nonlinear finite impulse response moving average systems. Math. Comput. Model. 55(3–4), 442–450 (2012)

14. J.H. Li, F. Ding, L. Hua, Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems. Nonlinear Dyn. 75(1–2), 235–245 (2014)

15. J.H. Li, W.X. Zheng, J.P. Gu, L. Hua, Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements. J. Frankl. Inst. 354(1), 316–331 (2017)

16. L. Ljung, System Identification: Theory for the User, 2nd edn. (Prentice Hall, Englewood Cliffs, New Jersey, 1999)

17. M. Lovera, T. Gustafsson, M. Verhaegen, Recursive subspace identification of linear and non-linear Wiener state-space models. Automatica 36(11), 1639–1650 (2000)

18. G. Mercère, L. Bako, Parameterization and identification of multivariable state-space systems: a canonical approach. Automatica 47(8), 1547–1555 (2011)

19. G. Mzyk, P. Wachel, Kernel-based identification of Wiener–Hammerstein system. Automatica 83, 275–281 (2017)

20. R.D. Nowak, Nonlinear system identification. Circuits Syst. Signal Process. 21(1), 109–122 (2005)

21. S. Oblak, I. ?krjanc, Continuous-time Wiener-model predictive control of a pH process based on a PWL approximation. Chem. Eng. Sci. 65(5), 1720–1728 (2010)

22. J. Pan, X. Jiang, X.K. Wan, W. Ding, A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems. Int. J. Control Autom. Syst. 15(3), 1189–1197 (2017)

23. T.B. Sch?n, A. Wills, B. Ninness, System identification of nonlinear state-space models. Automatica 47(1), 39–49 (2011)

24. G. Shafiee, M.M. Arefi, M.R. Jahed-Motlagh, A.A. Jalali, Nonlinear predictive control of a polymerization reactor based on piecewise linear Wiener model. Chem. Eng. J. 143(1–3), 282–292 (2008)

25. Y. Shi, J.H. Qin, H.S. Ahn, Distributed coordination control and industrial applications. IEEE Trans. Ind. Electron. 64(6), 4967–4971 (2017)

26. J. Sj?berg, Q.H. Zhang, L. Ljung, A. Benveniste, B. Delyon, P.-Y. Glorennec, H. Hjalmarsson, A. Juditsk, Nonlinear black-box modeling in system identification: a unified overview. Automatica 31(12), 1691–1724 (1995)

27. T. S?derstr?m, U. Soverini, Errors-in-variables identification using maximum likelihood estimation in the frequency domain. Automatica 79, 131–143 (2017)

28. K. Tiels, J. Schoukens, Wiener system identification with generalized orthonormal basis functions. Automatica 50(12), 3147–3154 (2014)

29. D.Q. Wang, Y.P. Gao, Recursive maximum likelihood identification method for a multivariable controlled autoregressive moving average system. IMA J. Math. Control Inf. 33(4), 1015–1031 (2016)

30. D.Q. Wang, Z. Zhang, J.Y. Yuan, Maximum likelihood estimation method for dual-rate Hammerstein systems. Int. J. Control Autom. Syst. 15(2), 698–705 (2017)

31. X.H. Wang, F. Ding, Joint estimation of states and parameters for an input nonlinear state-space system with colored noise using the filtering technique. Circuits Syst. Signal Process. 35(2), 481–500 (2016)

32. X.H. Wang, F. Ding, Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems. Int. J. Adapt. Control Signal Process. 30(6), 824–842 (2016)

33. D. Westwick, M. Verhaegen, Identifying MIMO Wiener systems using subspace model identification methods. Signal Process. 52(2), 235–258 (1996)

34. F. Yu, Z.Z. Mao, M.X. Jia, Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity. J. Process Control 23(8), 1108–1115 (2013)

35. L. Xu, F. Ding, Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling. Circuits Syst. Signal Process. 36(4), 1735–1753 (2017)

36. L. Xu, F. Ding, Y. Gu, A. Alsaedi, T. Hayat, A multi-innovation state and parameter estimation algorithm for a state space system with d-step state-delay. Signal Process. 140, 97–103 (2017)

37. W.X. Zhao, H.F. Chen, W.X. Zheng, Recursive identification for nonlinear ARX systems based on stochastic approximation algorithm. IEEE Trans. Autom. Control 55(6), 1287–1299 (2010)

38. W.X. Zhao, W.X. Zheng, E.-W. Bai, A recursive local linear estimator for identification of nonlinear ARX systems: Asymptotical convergence and applications. IEEE Trans. Autom. Control 58(12), 3054–3069 (2013)

在这38篇参考文献中，有18篇（2-6, 12-15, 22,29-32，35-38）是作者 Feng Ding, Ling Xu, Junlong Li, Wei Xing Zheng, Liang Hua, Wenfang Ding, D.Q. Wang的自引。 这些人都在不同的论文中是共同作者，相互关联。其他中国作者的关联关系，未作进一步挖掘。

（二） 引用（Citations）分析。

引用该文的文献如下：

1. Bingbing Shen, Feng Ding, Ling Xu and Tasawar Hayat. Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems, International Journal of Control, Automation and Systems, 2018, Volume 16, Number 5, Page 2225. [1 CITATIONS]

2. Cheng Wang, Kaicheng Li and Shuai Su. Hierarchical Newton Iterative Parameter Estimation of a Class of Input Nonlinear Systems Based on the Key Term Separation Principle Journal: Complexity, 2018, Volume 2018, Page 1. [1 CITATIONS]

3. Qinyao Liu, Feng Ding, Ling Xu and Erfu Yang. Partially coupled gradient estimation algorithm for multivariable equation-error autoregressive moving average systems using the data filtering technique, IET Control Theory & Applications, 2019, Volume 13, Number 5, Page 642. [11 CITATIONS]

4. Huafeng Xia, Yongqing Yang, Feng Ding, Ling Xu and Tasawar Hayat. Maximum likelihood gradient-based iterative estimation for multivariable systems, IET Control Theory & Applications, 2019, Volume 13, Number 11, Page 1683. [0 CITATIONS]

5. Ting Cui, Feng Ding, Xiangli Li and Tasawar Hayat. Kalman filtering based gradient estimation algorithms for observer canonical state-space systems with moving average noises, Journal of the Franklin Institute, 2019, Volume 356, Number 10, Page 5485. [0 CITATIONS]

6. Siyu Liu, Feng Ding, Ling Xu and Tasawar Hayat. Hierarchical Principle-Based Iterative Parameter Estimation Algorithm for Dual-Frequency Signals, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 7, Page 3251. [11 CITATIONS]

7. Rajalakshmi Murugesan, Jeyadevi Solaimalai and Karthik Chandran. Computer-Aided Controller Design for a Nonlinear Process Using a Lagrangian-Based State Transition Algorithm, Circuits, Systems, and Signal Processing, 2019. [0 CITATIONS]

8. Asma Atitallah, Sa?da Bedoui and Kamel Abderrahim. Joint Parameter and Time-Delay Identification Algorithm and Its Convergence Analysis for Wiener Time-Delay Systems, Circuits, Systems, and Signal Processing, 2019. [0 CITATIONS]

9. Mengting Chen, Feng Ding and Erfu Yang. Gradient-based iterative parameter estimation for bilinear-in-parameter systems using the model decomposition technique, IET Control Theory & Applications, 2018, Volume 12, Number 17, Page 2380. [2 CITATIONS]

10. Junhong Li and Xiao Li. Particle Swarm Optimization Iterative Identification Algorithm and Gradient Iterative Identification Algorithm for Wiener Systems with Colored Noise, Complexity, 2018, Volume 2018, Page 1. [1 CITATIONS]

11. Tiancheng Zong, Junhong Li, Xiao Li, Liangliang Shang and Xiaojiao Zhang. Parameter identification of Box-Jenkins systems based on the particle swarm optimization, Conference: 2019 Chinese Control And Decision Conference (CCDC), Year: 2019, Page 1696. [0 CITATIONS]

12. Jie Ding, Zhengxin Cao, Jiazhong Chen and Guoping Jiang. Weighted Parameter Estimation for Hammerstein Nonlinear ARX Systems, Circuits, Systems, and Signal Processing, 2019. [0 CITATIONS]

13. Lijuan Wan, Ximei Liu, Feng Ding and Chunping Chen. Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems, Mathematics, 2019, Volume 7, Number 7, Page 609. [2 CITATIONS]

14. Lijuan Liu, Feng Ding and Quanmin Zhu. Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise, International Journal of Systems Science, 2018, Volume 49, Number 13, Page 2763. [2 CITATIONS]

15. Ping Ma, Feng Ding and Tasawar Hayat. Multi-innovation gradient estimation algorithms for multivariate equation-error autoregressive moving average systems based on the filtering technique, IET Control Theory & Applications, 2019, Volume 13, Number 13, Page 2086. [0 CITATIONS]

16. Huafeng Xia, Yongqing Yang, Feng Ding, Ahmed Alsaedi and Tasawar Hayat. Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique, International Journal of Systems Science, 2019, Volume 50, Number 6, Page 1121. [0 CITATIONS]

17. Meihang Li, Ximei Liu and Feng Ding. The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle, International Journal of Adaptive Control and Signal Processing, 2019, Volume 33, Number 7, Page 1189. [5 CITATIONS]

18. Feng Ding, Xiao Zhang and Ling Xu. The innovation algorithms for multivariable state‐space models, Journal: International Journal of Adaptive Control and Signal Processing, 2019, Volume 33, Number 11, Page 1601. [0 CITATIONS]

19. Jian Pan, Hao Ma, Xiao Jiang, Wenfang Ding and Feng Ding. Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique, Complexity, 2018, Volume 2018, Page 1. [30 CITATIONS]

20. Qinyao Liu and Feng Ding. Auxiliary Model-Based Recursive Generalized Least Squares Algorithm for Multivariate Output-Error Autoregressive Systems Using the Data Filtering, Circuits, Systems, and Signal Processing, 2019, Volume 38, Number 2, Page 590. [22 CITATIONS]

21. Lijuan Liu, Yan Wang, Cheng Wang, Feng Ding and Tasawar Hayat. Maximum likelihood recursive least squares estimation for multivariate equation-error ARMA systems, Journal of the Franklin Institute, 2018, Volume 355, Number 15, Page 7609. [2 CITATIONS]

在21次引用中，有16次（[1, 3-6, 9-11, 13-21]）是Feng Ding, Ling Xu, Junhong Li进行引用的。非华裔作者的引用共2次，其他均为中国大陆作者引用。

5、结论

通过对Circuits, Systems, and Signal Processing 杂志推荐的近期出版的高引用论文分析发现，这些论文均为江南大学物联网学院丁锋（Feng Ding）及合作者完成的。研究的课题是传统的课题，作者发表的论文的所谓高引用统计，实际为作者及合作者疯狂的自我引用造成。这些论文实际上在出版商的网站的下载率极低。这是一种非常不诚实的学术不端行为。

根据上述引用情况分析，Feng Ding应该是中国引用率最高的10人之一。但是，用这种刷单方式获得的高引用率对评价科学家的学术水平和成果没有一点点可信度。只有同行评价才是最科学的评价方法。

近日，IET Signal Processing 收到了来自江南大学物联网学院的投稿，同样是研究类似的辨识问题。在prescreening阶段，主编在看了论文摘要后，针对摘要提出很多批评，建议不在送审。主编也把这篇文章分配给我来做prescreening（我自2007年以来一直是IET Signal Processing的变为/副编辑），我注意到论文引用的40篇参考文献中有10篇是引用Feng Ding的论文，认识到来自Feng Ding的团队。由于Feng Ding的学术不端行为曾经多次在新语丝曝光，加上老是研究老套的辨识问题，并没有看到什么实质性创新，我于是建议不再送审这篇论文。当然，我客气地建议作者改投控制类杂志。今天很高兴，看到Feng Ding 团队极少或几乎没有在IET Signal Processing 发表什么论文。

(XYS20191113)

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