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Proceedings Paper

Identification of nonlinear dynamic processes based on dynamic radial basis function networks
Author(s): Mihiar Ayoubi; Rolf Isermann
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Paper 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.

Paper Details

Date Published: 22 March 1996
PDF: 8 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235971
Show Author Affiliations
Mihiar Ayoubi, Technical Univ. of Darmstadt (Germany)
Rolf Isermann, Technical Univ. of Darmstadt (Germany)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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