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提出一种基于Ｗｉｅｎｅｒ模型辨识混沌系统的新方法。该方法利用三层前馈神经网络来辨识Ｗｉｅｎｅｒ模型中的静态非线性环节和学习混沌系统的内在规律性。同时给出了辨识混沌系统的结构和网络权值调整的学习算法。对Ｈｅｎｏｎ 系统的仿真结果表明，该方法是有效的。
A new method for identifying chaotic systems based on Wiener model is proposed. This method uses the three-layer feedforward neural network to identify the static nonlinearities in the Wiener model and to learn the inherent regularity of the chaotic system. At the same time, we give a learning algorithm to identify the structure of chaotic system and adjust the weights of network. The simulation results on Henon system show that this method is effective.