Recurrent neural control of a continuous bioprocess using first and second order learning

Authors:
Carlos-Román Mariaca-Gaspar;Julio-César Tovar Rodríguez;Floriberto Ortiz-Rodríguez
Affiliations:
Department of Communication and Electronic Engineering, Higher School of Mechanic and Electrical Engineering - Zacatenco, National Polytechnic Institute, Mexico D.F., Mexico;Department of Communication and Electronic Engineering, Higher School of Mechanic and Electrical Engineering - Zacatenco, National Polytechnic Institute, Mexico D.F., Mexico;Department of Communication and Electronic Engineering, Higher School of Mechanic and Electrical Engineering - Zacatenco, National Polytechnic Institute, Mexico D.F., Mexico
Venue:
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Year:
2012

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Abstract

The propose of this paper is to introduce a new Kalman Filter based in a Recurrent Neural Network topology (KFRNN) and a recursive Levenberg-Marquardt (L-M) algorithm. Such algorithm is able to estimate the states and parameters of a highly nonlinear continuous fermentation bioprocess in noisy environment. The control scheme is direct adaptive and also contains feedback and feedforward recurrent neural controllers. The proposed control scheme is applied for real-time identification and control of continuous stirred tank bioreactor model, taken from the literature, where a fast convergence, noise filtering and low mean squared error of reference tracking were achieved.