Learning of Chua's circuit attractors by locally recurrent neural networks

Many practical applications of neural networks require the identification o f strongly non-linear (e.g., chaotic) systems. In this paper, locally recur rent neural networks (LRNNs) are used to learn the attractors of Chua's cir cuit, a paradigm for studying chaos. LRNNs are characterized by a feed-forw ard structure whose synapses between adjacent layers have taps and Feedback connections. In general, the learning procedures of LRNNs are computationa lly simpler than those of globally recurrent networks. Results show that LR NNs can be trained to identify the underlying link among Chua's circuit sta te variables, and exhibit chaotic attractors under autonomous working condi tions. (C) 2001 Elsevier Science Ltd. All rights reserved.