A recurrent neural network for online design of robust optimal filters

Danchi Jiang, Jun Wang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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 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.

Original languageEnglish
Pages (from-to)921-926
Number of pages6
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume47
Issue number6
DOIs
Publication statusPublished - Jun 2000
Externally publishedYes

Keywords

  • Filter design
  • Neural networks

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