Abstract
This paper applied a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Marquardt (L-M) learning algorithm capable to estimate parameters 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 control 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.
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Baruch, I., Mariaca-Gaspar, CR., Barrera-Cortes, J., Castillo, O. (2010). Direct and Indirect Neural Identification and Control of a Continuous Bioprocess via Marquardt Learning. In: Castillo, O., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Intelligent Control and Mobile Robotics. Studies in Computational Intelligence, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15534-5_6
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DOI: https://doi.org/10.1007/978-3-642-15534-5_6
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