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Can I read A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations on EtoBox?
A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations by Jun Wang; Guang Wu is a Computer Science article available to read on EtoBox.
What is A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations about?
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.
Who reads A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Jun Wang; Guang Wu
- Publisher
- Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0893-6080)
- Published
- 1998
- Language
- EN
- Field
- Computer Science (Physical Sciences)