Share Email Print
cover

Proceedings Paper

Solution of nonlinear optimal control problems using modified Hopfield neural networks
Author(s): Daniel J. Stech
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Solution of nonlinear optimal control problems on analog parallel networks is proposed. Recurrent neural networks whose dynamic equations have a Lyapunov function are developed. Such circuits relax to an equilibrium which is the minimum of the Lyapunov function. Nonlinear optimal control problems are formulated in terms of a Lyapunov function and thus are solved using the recurrent networks. Convergence for linear and nonlinear classes of problems is considered. The method is demonstrated by developing and simulating a network to solve a nonlinear vibration problem. Simulation results demonstrate solution times are accurate and extremely fast. Solution times are shown to be independent of the size of the problem.

Paper Details

Date Published: 1 May 1994
PDF: 10 pages
Proc. SPIE 2192, Smart Structures and Materials 1994: Mathematics and Control in Smart Structures, (1 May 1994); doi: 10.1117/12.174238
Show Author Affiliations
Daniel J. Stech, U.S. Air Force Academy (United States)


Published in SPIE Proceedings Vol. 2192:
Smart Structures and Materials 1994: Mathematics and Control in Smart Structures
H. Thomas Banks, Editor(s)

© SPIE. Terms of Use
Back to Top