A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations

Jun Wang, Guang Wu

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

Abstract

A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. Analytical results on stability of the recurrent neural network and solvability of algebraic Riccati equations by use of the recurrent neural network are discussed. The operating characteristics of the recurrent neural network are also demonstrated through three illustrative examples.

Original languageEnglish
Pages (from-to)939-950
Number of pages12
JournalNeural Networks
Volume11
Issue number5
DOIs
Publication statusPublished - Jul 1998
Externally publishedYes

Keywords

  • Continuous-time algebraic equations
  • Recurrent neural networks

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