Automated recurrent neural network design of a neural controller in a custom power device

A general purpose implementation of the Tabu Search metaheuristic, called U niversal Tabu Search, is used to optimally design a Locally Recurrent Neura l Network architecture. Indeed, the design of a neural network is a tedious and time consuming trial and error operation that leads to structures whos e 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 I IR network synapses is formalised as an optimisation problem whose cost fun ction to be minimised is the network error calculated on a validation data set. The performances of the proposed approach have been tested on the desi gn problem of a Neural Network controller of a Custom Power protection devi ce.