Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks

Kambhampati, C., Garces, F. and Warwick, K. (2000) Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, pp. 64-69. ISBN 0769506194 doi: 10.1109/IJCNN.2000.857815

Abstract/Summary

This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.

Item Type	Book or Report Section
URI	https://reading-clone.eprints-hosting.org/id/eprint/21616
Item Type	Book or Report Section
Refereed	Yes
Divisions	Science
Uncontrolled Keywords	approximation theory, continuous time recurrent neural networks, identification, multidimensional system, multivariable system, nonautonomous dynamic systems, nonlinear dynamical system
Publisher	IEEE
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