A Tabu Search Algorithm for Optimal Sizing of Locally Recurrent Neural Networks

A general purpose implementation of the Tabu Search metaheuristic, called Universal Tabu Search, is used to optimally design a Locally Recurrent Neural Network architecture. In fact, generally, the design of a neural network is a tedious and time consuming trial and error operation that leads to structures whose optimality is not guaranteed. In this paper, the problem of choosing the number of hidden neurons and the number of taps and delays in the FIR and IIR network synapses is formalised as an optimisation problem whose cost function to be minimised is the network error calculated on a validation data set. The performance of the algorithm have been tested on the difficult task to learn the chaotic behaviour of a non linear circuit proposed by Chua as a paradigm for studying chaos.