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Trajectory tracking with dynamic neural networks

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3 Author(s)
Ferit Konar, A. ; Dept. of Comput. Eng., Sakarya Univ., Adapazari, Turkey ; Becerikli, Y. ; Samad, T.

The application of artificial neural networks to dynamical systems has been constrained by the non-dynamical nature of popular network architectures. Many of the difficulties that ensue-large network sizes, long training times, the need to predetermine buffer lengths-can be overcome with dynamic neural networks. The minimization of a quadratic performance index is considered for trajectory tracking or process simulation applications. Two approaches for gradient computation are discussed: forward and adjoint sensitivity analysis. The computational complexity of the latter is significantly less, but it requires a backward integration capability. We also discuss two parameter updating methods: the gradient descent method and Levenberg-Marquardt approach

Published in:

Intelligent Control, 1997. Proceedings of the 1997 IEEE International Symposium on

Date of Conference:

16-18 Jul 1997