Identifying chaotic systems using Wiener and Hammerstein cascade models

This paper describes two basic structures for identifying chaotic systems based on the Wiener and Hammerstein cascade models, in which three-layer feedforward artificial neural network is employed as the nonlinear static subsystem and a simple linear plant is used as the dynamic subsystem. Through training of the neural network and choosing an appropriate linear subsystem, various chaotic systems can be well identified by these two basic structures. Computer simulation results on Henon and Lozi systems are presented to demonstrate the effectiveness of these proposed structures. It is also shown that two chaotic systems whose outputs are different can actually exhibit similar chaotic attractors.