Use of Hopfield Neural Networks in Optimal Guidance
This document has been relocated to http://scholarsmine.mst.edu/mec_aereng_facwork/3391
There were 1 downloads as of 27 Jun 2016.
Abstract
A Hopfield neural network architecture is developed to solve the optimal control problem for homing missile guidance. A linear quadratic optimal control problem is formulated in the form of an efficient parallel computing device known as a Hopfield neural network. Convergence of the Hopfield network is analyzed from a theoretical perspective, showing that the network, as a dynamical system approaches a unique fixed point which is the solution to the optimal control problem at any instant during the missile pursuit. Several target-intercept scenarios are provided to demonstrate the use of the recurrent feedback neural net formulation.
