A recurrent neural network for online design of robust optimal filters

被引：3
|
作者：
Jiang, DC
Wang, J
机构：
[1] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Dept Mech Automat Engn, Shatin, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS | 2000年 / 47卷 / 06期
关键词：
filter design; neural networks;
D O I：
10.1109/81.852947
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A recurrent neural network is developed for robust optimal filter design. The purpose is to fill the gap between the real-time computation requirement in practice and the computational complexity of the filter design in the case that the statistical properties of noise are unknown. First, an H-infinity requirement and an L-2 requirement of filter design problem are formulated as a group of linear matrix inequalities. On this basis, an optimization problem is introduced to solve the robust optimal; filter design problem, Then, a recurrent neural network is deliberately developed for solving the optimization problem in real time, The effectiveness and efficiency of the recurrent neural network is shown by use of theoretical and simulation results.
引用
收藏
页码：921 / 926
页数：6
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