Identification of nonlinear dynamic processes based on dynamic radial basis function networks

Mihiar Ayoubi; Rolf Isermann

doi:10.1117/12.235971

22 March 1996 Identification of nonlinear dynamic processes based on dynamic radial basis function networks

Mihiar Ayoubi, Rolf Isermann

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235971
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

An attempts has been made to establish a discrete-time neuron model with a radial basis function. The neuron is utilized to build RBF-networks with locally distributed dynamics to identify input/output models of dynamic nonlinear processes. The adaptation algorithm which ascertains the optimal network parameters is provided. Further, an enhanced parameter estimation algorithm is derived, the so-called compound estimation procedure, which combines elaborated least squares techniques to highly decrease the training times. The proposed neural model is applied to identify black-box models of a turbocharging process within a Diesel engine. Benefits and drawbacks of the proposed neural structure are worked out.

Citation Download Citation

Mihiar Ayoubi and Rolf Isermann "Identification of nonlinear dynamic processes based on dynamic radial basis function networks", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235971

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