A Class of Modified Hopfield Networks for Control of Linear and Nonlinear Systems
This document has been relocated to http://scholarsmine.mst.edu/mec_aereng_facwork/3335
There were 18 downloads as of 27 Jun 2016.
Abstract
This paper presents a class of modified Hopfield neural networks (MHNN) and their use in solving linear and nonlinear control problems. This class of networks consists of parallel recurrent networks which have variable dimensions that can be changed to fit the problems under consideration. It has a structure to implement an inverse transformation that is essential for embedding optimal control gain sequences. Equilibrium solutions are discussed. Numerical results for a motivating aircraft control problem (linear) are presented. Furthermore, we formulate the state-dependent Riccati equation method (SDRE) for a class of nonlinear dynamical system and show how MHNN provides the solution. Two examples that illustrate the potential of this network for the SDRE method are also presented.
