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Affine modeling of nonlinear multivariable processes using a new adaptive neural network-based approach

✍ Scribed by Amin Sabet Kamalabady; Karim Salahshoor

Publisher: Elsevier Science
Year: 2009
Tongue: English
Weight: 786 KB
Volume: 19
Category: Article
ISSN: 0959-1524
DOI: 10.1016/j.jprocont.2008.07.001

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✦ Synopsis

This paper presents a new method for on-line identification of exact affine model for multivariable processes with nonlinear and time-varying behaviors. A self-generating radial basis function (RBF) neural network trained by growing and pruning algorithm for RBF (GAP-RBF) is utilized for deriving the affine model. The extended Kalman filter (EKF) is used for parameter adaptation in the GAP-RBF neural network. The growing and pruning criteria of the original GAP-RBF have been modified with the objective to enhance its performance in on-line identification. Simulation results on two nonlinear multivariable CSTR benchmark problems show an excellent performance of the proposed approach, incorporated with the modified GAP-RBF (MGAP-RBF) neural network, for affine modeling.

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