Automated Recurrent Neural Network Design to Model the Dynamics of Complex Systems

A general purpose implementation of the tabu search metaheuristic, called Universal Tabu Search, is used to optimally design a locally recurrent neural network architecture. 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 proposed approach has been tested on the problem of modelling the dynamics of a non-isothermal, continuously stirred tank reactor, in two different operating conditions: when a first order exothermic reaction is occurring; and when two consecutive first order reactions lead to a chaotic behaviour. Comparisons with alternative neural approaches are reported, showing the usefulness of the proposed method.