Adaptive nonlinear neuro-controller with an integrated evaluation algorithm for nonlinear active noise systems

Journal of Sound and Vibration

Volumn 329, Issue 24, 2010, Pages 5005-5016

  Zhang, Xinghua ^a   Ren, Xuemei ^a   Na, Jing ^a   Zhang, Bo ^a   Huang, Hong ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FILTERED-X; GRADIENT DESCENT ALGORITHMS; INFORMATION ENTROPY; INTEGRATED EVALUATION; LEARNING PARAMETERS; NOISE SYSTEMS; NONLINEAR ACTIVE NOISE CONTROL; NONLINEAR AND NON-GAUSSIAN; NONLINEAR MEMORY; PERFORMANCE INDICES; QUADRATIC ERRORS; SIMPLE STRUCTURES; SIMULATION RESULT; SINGLE NEURON; WEIGHTED SUM; WIENER MODELS;

ACTIVE NOISE CONTROL; ADAPTIVE ALGORITHMS; COMPUTATIONAL COMPLEXITY; CONTROLLERS; FUZZY CONTROL; GAUSSIAN DISTRIBUTION;

ADAPTIVE CONTROL SYSTEMS;

EID: 77955431402     PISSN: 0022460X     EISSN: 10958568     Source Type: Journal    
DOI: 10.1016/j.jsv.2010.06.017     Document Type: Article

References (30)

1. S.J. Elliott Signal Processing for Active Control 2001 Academic Press London
2. S.M. Kuo, and D.R. Morgan Active noise control: a tutorial review Proceeding of IEEE 87 1999 943 973
3. X. Sun, S.M. Kuo, and M. Guang Adaptive algorithm for active noise control of impulsive noise Journal of Sound and Vibration 291 2006 516 522 (Pubitemid 43069463)
4. R.S. Sanchez, M.A. Cuguero, A. Masip, J. Quevedo, and V. Puig Robust identification and feedback design: an active noise control case study Control Engineering Practice 16 2008 1265 1274
5. M.T. Akhtara, and W. Mitsuhashib Improving performance of FxLMS algorithm for active noise control of impulsive noise Journal of Sound and Vibration 327 2009 647 656
6. D.P. Das, and G. Panda Active mitigation of nonlinear noise processes using a novel filtered-s LMS algorithm IEEE Transactions on Speech and Audio Processing 12 2004 313 322
7. D. Zhou, and V. DeBrunner Efficient adaptive nonlinear filters for nonlinear active noise control IEEE Transactions on Circuits and Systems: Regular Papers 54 2007 669 681
8. L. Tan, and J. Jiang Adaptive Volterra filters for active control of nonlinear noise processes IEEE Transactions on Signal Processing 49 2001 1667 1676
9. Y.L. Zhou, Q.Z. Zhang, X.D. Li, and W.S. Gan Analysis and DSP implementation of an ANC system using a filtered-error neural network Journal of Sound and Vibration 285 2005 1 25
10. M. Bouchard New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks IEEE Transactions on Neural Networks 12 2001 135 147
11. C.Y. Chang, and F.B. Luoh Enhancement of active noise control using neural-based filtered-X algorithm Journal of Sound and Vibration 305 2007 348 356
12. M.O. Tokhi, and R. Wood Active noise control using radial basis function networks Control Engineering Practice 5 1997 1311 1322 (Pubitemid 27477483)
13. S. Manikandan, and M. Madheswaran A new design of active noise feedforward control systems using delta rule algorithm Information Technology Journal 6 2007 1162 1165
14. Q.Z. Zhang, W.S. Gan, and Y.L. Zhou Adaptive recurrent fuzzy neural networks for active noise control Journal of Sound and Vibration 296 2006 935 948
15. H.Q. Zhao, X.P. Zeng, and J.S. Zhang Adaptive reduced feedback FLNN filter for active control of nonlinear noise processes Signal Processing 90 2010 834 847
16. H. Yue, and H. Wang Minimum entropy control of closed-loop tracking errors for dynamics stochastic systems IEEE Transactions on Automatic Control 48 2003 118 122
17. D. Erdogmus, Information Theoretic Learning: Renyi's Entropy and its Applications to Adaptive System Training, PhD Thesis, University of Florida, 2002.
18. D. Erdogmus, K.E. Hild, and J.C. Principe Blind source separation using Renyi's-marginal entropy Neurocomputing 49 2002 25 38
19. L. Guo, and H. Wang Minimum entropy filtering for multivariate stochastic system with non-Gaussian noises IEEE Transactions on Automatic Control 51 2007 695 700
20. P. Afshar, H. Wang, ILC-based adaptive minimum entropy control for general stochastic systems using neural networks, Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, December 2008, pp. 252257.
21. D. Erdogmus, and J.C. Principe Convergence properties and data efficiency of the minimum error entropy criterion in adaline training IEEE Transactions on Signal Processing 51 2007 1966 1978
22. B.D. Chen, J.C. Hu, Z.Q. Sun, A joint stochastic gradient algorithm and its application to system identification with RBF networks, Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA), Dalian, China, June 2006, pp. 17541758.
23. V.J. Mathews, and G.L. Sicuranza Sicuranza, Polynomial Signal Processing 2000 John Wiley & Sons Inc. New York
24. E.W. Bai, and D. Li Convergence of the iterative Hammerstein system identification algorithm IEEE Transactions on Automatic Control 49 2004 1929 1940
25. G. Belforte, and P. Gay Discrete time Hammerstein model identification with unknown but bounded errors IET Proceedings Control Theory and Applications 148 2001 523 529
26. M. Sznaier Computational complexity analysis of set membership identification of Hammerstein and Wiener systems Automatica 45 2009 701 705
27. X. Hong, and R.J. Mitchell Hammerstein model identification algorithm using BezierBernstein approximation IET Control Theory and Applications 1 2007 1149 1159
28. Y.B. Zhao, G.P. Liu, and D. Rees Networked predictive control systems based on the Hammerstein model IEEE Transactions on Circuits and Systems 55 2008 469 473
29. E. Parzen On the estimation of a probability density function and model Annals of Mathematical Statistics 33 1962 1065 1076
30. Y.G. Niu, J. Lam, X.Y. Wang, and D.W.C. Ho Neural adaptive sliding model control for a class of nonlinear neutral delay systems Journal of Dynamic Systems, Measurement and Control 130 2008 061011