USE OF RECURSIVE STOCHASTIC ALGORITHM FOR NEURAL NETWORKS SYNTHESIS

A new method based on a recursive stochastic algorithm is presented fo r neural networks synthesis. The cost function of the difference betwe en the output of the net and the output of the process to be modelled by the net has nonunique stationary points. The common optimization te chniques lead to local optima. It is shown that the solution of this o ptimization problem is connected with the construction of a convex env elope, characterized by local extrema of the initial problem. Then a r ecursive stochastic random search algorithm is derived for finding the optimum using realizations of a random variable associated with the f unction to be minimized. The application of this method is illustrated by an experimental example concerning neural networks synthesis for a n industrial calcinator.