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.<>
Use of Hopfield neural networks in optimal guidance
IEEE Transactions on Aerospace and Electronic Systems ; 30 , 1 ; 287-293
1994-01-01
710668 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Use of Hopfield Neural Networks in Optimal Guidance
Online Contents | 1994
|Use of Hopfield neural networks in optimal guidance
AIAA | 1991
|Binary optimization by Hopfield neural networks
Kraftfahrwesen | 2004
|Binary Optimization by Hopfield Neural Networks
British Library Conference Proceedings | 2004
|Binary Optimization by Hopfield Neural Networks
SAE Technical Papers | 2004
|