非线性离散系统的神经网络结构识别

Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics Pub Date : 1989-11-14 DOI:10.1109/ICSMC.1989.71469

A. M. Elramsisi, M. Zohdy, N. Loh

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引用次数: 1

摘要

提出了一种识别非线性离散系统模型结构和参数的方法。该结构用Gabor基函数(gbf)的频位域表示。本文还提出了一种gbf的简化方法，将gbf的空间高斯包络替换为三角形包络。为了抑制噪声对程序的影响，还引入了对gbf的修改。描述了一种带有非均匀采样的三层神经网络，用于解决系统辨识问题。

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Structure recognition of nonlinear discrete-time systems by neural networks

A technique is proposed to identify the structure as well as the parameters of nonlinear discrete-time system models. The structure is represented in a frequency-position domain of Gabor basis functions (GBFs). A simplification to the GBFs is also presented, where the spatial Gaussian envelope of GBFs is replaced with a triangular one. A modification to the GBFs has also been introduced in order to suppress noise effects on the procedure. A three-layered neural network, augmented with nonuniform sampling, is described for solving the system identification problem.<>

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Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics

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