Learning of Chuaʹs circuit attractors by locally recurrent neural networks

Many practical applications of neural networks require the identification of strongly non-linear (e.g., chaotic) systems. In this paper, locally recurrent neural networks (LRNNs) are used to learn the attractors of Chuaʹs circuit, a paradigm for studying chaos. LRNNs are characterized by a feed-forward structure whose synapses between adjacent layers have taps and feedback connections. In general, the learning procedures of LRNNs are computationally simpler than those of globally recurrent networks. Results show that LRNNs can be trained to identify the underlying link among Chuaʹs circuit state variables, and exhibit chaotic attractors under autonomous working conditions.