Adaptive noise cancellation using enhanced dynamic fuzzy neural networks

In this paper, a novel adaptive noise cancellation algorithm using enhanced dynamic fuzzy neural networks (EDFNNs) is described. In the proposed algorithm, termed EDFNN learning algorithm, the number of radial basis function (RBF) neurons (fuzzy rules) and input-output space clustering is adaptively determined. Furthermore, the structure of the system and the parameters of the corresponding RBF units are trained online automatically and relatively rapid adaptation is attained. By virtue of the self-organizing mapping (SOM) and the recursive least square error (RLSE) estimator techniques, the proposed algorithm is suitable for real-time applications. Results of simulation studies using different noise sources and noise passage dynamics show that superior performance can be achieved.

[1]  Meng Joo Er,et al.  A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks , 2001, IEEE Trans. Fuzzy Syst..

[2]  Kwang Bo Cho,et al.  Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction , 1996, Fuzzy Sets Syst..

[3]  Meng Joo Er,et al.  Dynamic fuzzy neural networks-a novel approach to function approximation , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[4]  N. Sundararajan,et al.  Minimal resource allocation network for adaptive noise cancellation , 1999 .

[5]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[6]  Y Lu,et al.  A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks , 1997, Neural Computation.

[7]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[8]  Chia-Feng Juang,et al.  A recurrent self-organizing neural fuzzy inference network , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[9]  Stephen A. Billings,et al.  Recurrent radial basis function networks for adaptive noise cancellation , 1995, Neural Networks.

[10]  Sukhan Lee,et al.  A Gaussian potential function network with hierarchically self-organizing learning , 1991, Neural Networks.

[11]  Zheng Rong. Li,et al.  Adaptive noise cancellation using soft computing approach , 2006 .

[13]  B. Friedlander,et al.  System identification techniques for adaptive noise cancelling , 1982 .

[14]  O. Kipersztok.  Active control of broadband noise using fuzzy logic , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[15]  O. Kipersztok,et al.  Fuzzy active control of a distributed broadband noise source , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[16]  Chia-Feng Juang,et al.  An adaptive neural fuzzy filter and its applications , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[17]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..