Authors: Erivelton Geraldo Nepomuceno
Addresses: Departamento de Engenharia Elétrica, Universidade Federal de São João del-Rei, Praça Frei Orlando, 170 – Centro, 36307-352, São João del-Rei, MG, Brazil
Abstract: This paper presents a multiobjective learning algorithm for the random neural network (RNN). A learning process consists in updating the parameters of the RNN. In a monoobjective approach, this update is made by means of the minimisation of one objective function, which is usually quadratic error of input-output. Here, we state a general procedure to update the parameters that take into account more than one objective. The parameter estimation is described as a multiobjective optimisation problem (MOP) and it is solved using weighting problem and gradient descent. The solution of MOP is a set called Pareto-set. A case study is presented, where quadratic error of dynamic input-output and the quadratic error of static curve were used to estimate the parameters of the RNN. We show that multiobjective learning improves the quality of models built from limited-range dynamic data.
Keywords: random neural networks; multiobjective optimisation; Pareto set; system identification; parameter estimation.
International Journal of Advanced Intelligence Paradigms, 2014 Vol.6 No.1, pp.66 - 80
Published online: 28 Jun 2014 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article