Time-Varying neurocomputing: an iterative learning perspective
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
| Hi-index | 0.00 |
This paper presents a neural network framework for implementing unknown time-varying mappings. A unified architecture of time-varying neural networks is proposed, and the methodology of iterative learning is used for the network training. Convergence results of the iterative learning least squares algorithm are derived under assumption of bounded input signals. Periodic neural networks are explored as well to be used as periodic function approximation tools.