摘要
Four optimal approaches of high-order finite-impulse response(FIR) digital filters were developed for designing four types filters using neural network algorithms. The solutions were presented as parallel algorithms to approximate the desired frequency response specification. Therefore, these methods avoid matrix inversion, and make a fast calculation of the filter’s coefficients possible. The convergence theorems of these proposed algorithms were presented and proved to illustrate them stable, and the implementation of these methods was described together with some design guidelines. The simulation results show that the ripples of the designed FIR filters are significantly little in the pass-band and stop-band, and the proposed algorithms are of fast convergence.
Four optimal approaches of high-order finite-impulse response(FIR) digital filters were developed for designing four types filters using neural network algorithms. The solutions were presented as parallel algorithms to approximate the desired frequency response specification. Therefore, these methods avoid matrix inversion, and make a fast calculation of the filter's coefficients possible. The convergence theorems of these proposed algorithms were presented and proved to illustrate them stable, and the implementation of these methods was described together with some design guidelines. The simulation results show that the ripples of the designed FIR filters are significantly little in the pass-band and stop-band, and the proposed algorithms are of fast convergence.
基金
Project (50677014) supported by the National Natural Science Foundation of China
project (20060532002) supported by the Doctoral Special Fund of Ministry of Education, China
project (NCET-04-0767) supported by the Program for New Century Excellent Talents in University
projects(06JJ2024, 03GKY3115, 04FJ2003, and 05GK2005) supported by the Foundation of Hunan Provincial Science and Technology
关键词
脉冲响应
数字滤波器
频率响应
神经网络
high-order finite-impulse response digital filter
frequency response
neural network
convergence theorem