Parameter estimation using artificial neural nets

Abstract

Artificial neural nets have become increasingly popular in solving optimization problems such as the Travelling Salesman Problem, A/D converter realization, linear programming and pattern recognition. In many of these optimization problems, the neural net structure is based on the Hopfield model and each neuron is assumed to have two distinct outputs, 0 or 1. In this paper, a continuous Hopfield net is used to formulate and solve a least squares minimization problem associated with the parameter estimation of a physical system. The algorithm shows good convergence and a certain amount of `recursion' can be incorporated which makes it useful for real time applications.