A Neural Networks Approach for Designing FIR Notch Filters
Xiaohua Wang, Yigang He, Yulou Peng, Jie Xiong · 2006
This paper addresses the design problem of linear-phase finite-impulse response (FIR) notch filters based on a neural networks optimization technique. The main idea is to minimize the weighted square-error function in the frequency-domain. The convergence theorem of the neural networks algorithm is proved to illustrate the proposed algorithm stable, and the implementation of the approach is also described together with some design guidelines. The solution is presented as a parallel algorithm to approximate the desired frequency response specification. Thus, the method avoids matrix inversion, and makes a fast calculation of the filter's coefficients possible. Some optimal design examples are given to demonstrate the effectiveness of the proposed design method