Performance analysis of locally recurrent neural networks

Many practical applications of neural networks require the identification of nonlinear deterministic systems or chaotic systems. In these cases the use of a network architecture known as locally recurrent neural network (LRNN) is often preferable in place of standard feedforward multi-layer perceptron (MLP) networks, or of globally recurrent neural network. In this paper locally recurrent networks are used to simulate the behaviour of the Chua's circuit that can be considered a paradigm for studying chaos. It is shown that such networks are able to identify the underlying link among the state variables of the Chua's circuit. Moreover, they are able to behave like an autonomous Chua's double scroll, showing a chaotic behaviour of the state variables obtainable through a suitable circuit elements choice.