Identification of chaotic systems using a self-constructing recurrent neural network

This paper presents a self-constructing recurrent neural network (SCRNN) capable of building itself with a compact structure from input-output measurements for identification of chaotic systems. The proposed SCRNN is constituted by a static nonlinear network cascaded with a linear dynamic network. A unified learning algorithm consisting of two mechanisms, a hybrid weight initialization method and a parameter optimization method, has been developed for the structure and parameter identification. With this learning algorithm, the SCRNN is exempted from trial and error in structure initialization as well as parameterization. Computer simulations on discrete-time chaotic systems, including logistic and Henon mappings, validate that the proposed SCRNN is capable of capturing the dynamical behavior of chaotic systems with a compact network size.