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

Baruch I., Mariaca-Gaspar C. R.

Journal of Automation Mobile Robotics and Intelligent Systems

2010

Vol. 4, No. 4

37-52

This paper applies a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Mar quardt (L-M) learning algorithm capable to estimate para meters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural con trol schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.