Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

Authors:
Luiz Augusto da Cruz Meleiro;Fernando José Von Zuben;Rubens Maciel Filho
Affiliations:
Department of Food Engineering, Federal Rural University of Rio de Janeiro, Caixa Postal 74573, CEP 23890-971 Seropedica, RJ, Brazil;School of Electrical and Computer Engineering, State University of Campinas, FEEC-UNICAMP, Caixa Postal 6101, CEP 13083-970 Campinas, SP, Brazil;School of Chemical Engineering, State University of Campinas, FEQ-UNICAMP, Caixa Postal 6066, CEP 13081-970 Campinas, SP, Brazil
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

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Abstract

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.