A recurrent neural network for online design of robust optimal filters

Author

Jiang, Danchi ; Wang, Jun

Author_Institution

Chinese Univ. of Hong Kong, Shatin, China

Volume

47

Issue

6

fYear

2000

fDate

6/1/2000 12:00:00 AM

Firstpage

921

Lastpage

926

Abstract

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_∞ requirement and an L₂ requirement of the 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

Keywords

H^∞ optimisation; circuit CAD; computational complexity; filtering theory; linear systems; matrix algebra; recurrent neural nets; H_∞ requirement; L₂ requirement; computational complexity; linear matrix inequalities; online design; optimal filter design; optimization problem; real-time computation requirement; recurrent neural network; robust optimal filters; Computational complexity; Design methodology; Design optimization; Linear matrix inequalities; Neural networks; Noise robustness; Nonlinear filters; Recurrent neural networks; Riccati equations; Signal processing;

fLanguage

English

Journal_Title

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on

Publisher

ieee

ISSN

1057-7122

Type

jour

DOI

10.1109/81.852947

Filename

852947