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

• Autorzy: Baruch I. , Mariaca-Gaspar C. R.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

backpropagation learning; direct adaptive neural control; indirect adaptive sliding mode control; Kalman filter recurrent neural network identifier; Levenberg-Marquardt learning

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2010
• Tom: Vol. 4, No. 4
• Strony: 37--52
• Opis fizyczny: Bibliogr. 14 poz.,rys.

Twórcy

autor

Baruch I.

autor

Mariaca-Gaspar C. R.

• Department of Automatic Control, CINVESTAV-IPN, Av. IPN No 2508, 07360 Mexico City, Mexico. Phone: [phone]/ext. 42-29,

Bibliografia

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