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Proceedings Paper

Solution of nonlinear optimal control problems using modified Hopfield neural networks
Author(s): Daniel J. Stech
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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)

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