Optimal Design on FIR Digital Filters Using the Parallel Algorithm of Neural Networks

摘要：

This paper introduces in detail the optimal design approach of high-order FIR digital filter and differentiator using the algorithm of neural networks. The main idea is to minimize the sum of the square errors between the amplitude response of the ideal FIR digital filter or digital differentiator and that of the designed by training the weight vector of neural networks, then obtaining the impulse response of FIR digital filter or differentiator. The convergence theorem of the neuralnetwork algorithm is presented and proved, and the optimal design approach is introduced by examples of high-order FIR digital filter and digital differentiator. The results show that the high-order FIR digital filter or digital differentiator designed by training the weights of neural networks has a very high precision and very fast convergence speed, and initial weights are stochastic. Therefore, the presented optimum design method in the paper is significantly effective.

作者: Zeng Zhe-zhao Chen Ye Zhu Wei Wang Yao-nan

作者单位: School of Electrical & Information Engineering Changsha University of Science & Technology Changsha, School of Electrical & Information Engineering Hunan University Changsha, Hunan, China

会议类型: 国际会议

会议名称: 2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)

会议地点: 广西桂林

会议语种:英文

页码: 191-195

在线出版日期: 2006-06-25（万方平台首次上网日期，不代表论文的发表时间）