An adaptive RBFN-based filter for adaptive noise cancellation

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.