A Duplex Neurodynamic Learning Approach to Modeling Nonlinear Systems

Xiang Huang,Hai-Tao Zhang,Guanrong Chen,Jun Wang

IEEE Transactions on Systems, Man, and Cybernetics（2024）

the MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems | School of Artificial Intelligence and Automation | City Univ Hong Kong

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Abstract

Data-based discovery of the underlying dynamics of nonlinear systems is of great importance to the prediction and control of engineering systems. This article presents a duplex neurodynamic learning (DNL) approach to the identification of discrete-time nonlinear systems subjected to both external disturbances and measurement noise. A neurodynamic learning method is proposed based on two-timescale recurrent neural networks (RNNs) for system identification. Truncated singular value decomposition is adopted to purify the data contaminated by external disturbances and measurement noises. Two RNNs are employed to cooperatively search for a global optimal solution, and the particle swarm optimization rule is used to reinitialize the RNNs upon the local convergence of the RNNs. The effectiveness and superiority of the proposed DNL method are demonstrated via simulations on benchmark chaotic and NARMAX systems.

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Identification,neural networks,nonlinear estimation,optimization methods,Identification,neural networks,nonlinear estimation,optimization methods

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【要点】：本文提出了一种双时间尺度递归神经网络为基础的双层神经动态学习（DNL）方法，用于识别受外部干扰和测量噪声影响的离散时间非线性系统，并通过截断奇异值分解净化数据，实现了全局最优解的搜索。

【方法】：通过构建两个递归神经网络（RNNs）并采用截断奇异值分解技术以及粒子群优化规则来提高系统识别的准确性。

【实验】：通过在标准混沌系统和NARMAX系统上的模拟实验，证明了所提DNL方法的有效性和优越性，但未提及具体的数据集名称。