This paper proposes a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Marquardt (L-M) second order learning algorithm capable to estimate parameters and states of highly nonlinear bioprocess in a noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct adaptive neural control scheme. The proposed control scheme was applied for real-time soft computing 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. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Baruch, I., Mariaca-Gaspar, C. R., & Barrera-Cortes, J. (2009). Direct adaptive soft computing neural control of a continuous bioprocess via second order learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5845 LNAI, pp. 500–511). https://doi.org/10.1007/978-3-642-05258-3_44
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