A neural-network approach to modeling and analysis

Chen-Yuan Chen, Cheng-Wu Chen, W. Chiang, Jing-Dong Hwang
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Abstract

A backpropagation network can always be used in modeling. This study is concerned with the stability problem of a neural network (NN) system which consists of a few subsystems represented by NN models. In this paper, the dynamics of each NN model is converted into linear inclusion representation. Subsequently, based on the representations, the stability conditions in terms of Lyapunov's direct method is derived to guarantee the asymptotic stability of NN systems.
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建模和分析的神经网络方法
反向传播网络总是可以用于建模。本文研究由神经网络模型表示的若干子系统组成的神经网络系统的稳定性问题。在本文中,每个神经网络模型的动态被转换成线性包含表示。在此基础上,导出了用Lyapunov直接方法表示的稳定性条件,以保证神经网络系统的渐近稳定性。
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