An adaptive RBFN-based filter for adaptive noise cancellation

Author

Li, Zhengrong ; Er, Meng Joo ; Gao, Yang

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

6

fYear

2003

fDate

9-12 Dec. 2003

Firstpage

6175

Abstract

In this paper, a new adaptive radial-basis-function-networks- (RBFN-) based filter for the adaptive noise cancellation (AXC) problem is proposed. The algorithm of structure identification and parameters adjustment is developed. The proposed RBFN-based filtering approach implements Takagi-Sugeno-Kang (TSK) fuzzy systems functionally. The RBFN-based filter has three major features: (1) no space pre-partitioning is needed; (2) no predetermination, such as the number of RBF neurons (fuzzy rules), must be given; (3) fast learning speed is achieved. Simulation results demonstrate that the proposed adaptive RBFN-based filter can cancel the noise successfully and efficiently with a parsimonious structure.

Keywords

adaptive filters; filtering theory; fuzzy systems; learning (artificial intelligence); radial basis function networks; self-adjusting systems; signal denoising; adaptive filters; adaptive noise cancellation; fuzzy systems; online structure learning; radial-basis-function-networks based filter; self-organizing algorithm; Adaptive filters; Backpropagation algorithms; Clustering algorithms; Filtering algorithms; Fuzzy neural networks; Neural networks; Neurons; Noise cancellation; Nonlinear filters; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2003. Proceedings. 42nd IEEE Conference on

ISSN

0191-2216

Print_ISBN

0-7803-7924-1

Type

conf

DOI

10.1109/CDC.2003.1272264

Filename

1272264