Automated Recurrent Neural Network Design of a Neural Controller in a Custom Power Device

Authors:
B. Cannas;G. Celli;A. Fanni;F. Pilo
Affiliations:
Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy;Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy;Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy;Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
Venue:
Journal of Intelligent and Robotic Systems
Year:
2001

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Abstract

A general purpose implementation of the Tabu Search metaheuristic, called Universal Tabu Search, is used to optimally design a Locally Recurrent Neural Network architecture. Indeed, 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 performances of the proposed approach have been tested on the design problem of a Neural Network controller of a Custom Power protection device.