Zhengrong

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An Adaptive RBFN-based Filter for Adaptive Noise Cancellation

Zhengrong Li, Meng Joo Er, and Yang Gao, “An Adaptive RBFN-based Filter for Adaptive Noise Cancellation,” in Proc. 42nd IEEE Conference on Decision and Control, pp. 6175-6180, USA, 2003.

Abstract:In this paper, a new adaptive radial-basis-function-networks- (RBFN-) based filter for the adaptive noise cancellation (ANC) 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.