학술논문
Adaptation of diagonal recurrent neural network model.
Document Type
Article
Author
Source
Subject
*ARTIFICIAL neural networks
*KALMAN filtering
*ALGORITHMS
*ADAPTIVE computing systems
*ARTIFICIAL intelligence
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Language
ISSN
0941-0643
Abstract
An adaptive direct recurrent neural network model is developed for nonlinear dynamic system modelling in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. The effectiveness of the developed adaptive model is demonstrated by applying to modelling a simulated continuous stirred tank reactor (CSTR). The model converges to the new process dynamics very quickly after a constant disturbance is added, and therefore can be used as an adaptive model in the adaptive model predictive control or internal model control for time-varying systems or fault tolerant control of nonlinear systems. [ABSTRACT FROM AUTHOR]