Automated neural model design for a CSTR using a Tabu Search algorithm
B. Cannas, Alessandra Fanni, Michele Marchesi, Fabrizio Pilo · 1999
: 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 performances of the proposed approach have been tested on the problem of modelling non isothermal, continuously stirred tank reactor, in which a first order exothermic reaction is occurring. Comparison with alternative neural approaches are reported, showing the usefulness of the proposed method. I. INTRODUCTION Many engineering problems such as, for instance, ti...