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
Full text not archived in this repository.
To link to this item DOI: 10.1109/IJCNN.2000.857815
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.