Combining wavelet networks with a kind method of model structure design and parameter estimation of NARMAX model, a modified wavelet network training scheme is proposed.
将基于MGS算法的NARMAX模型结构与参数辨识的一体化算法和小波网络相结合，提出一种改进的小波网络学习算法。
Based on wavelet networks and multiple model adaptive control theories, the identification and control methods for the complicated nonlinear dynamic systems are proposed.
本文以小波网络和多模型理论为基础，对复杂非线性动态系统的辨识和控制方法进行了研究。
Several fixed models with one adaptive model are used as sub-models in this method. And the wavelet networks controllers are designed according to the corresponding identifiers.
该方法使用一个自适应小波网络和多个固定小波网络作为子模型，针对每个子模型设计相应的小波网络控制器。
The properties of the wavelet networks are analyzed. According to the approximation ability of wavelet networks, the nonlinear static system and the nonlinear dynamic system can be identified.
分析了小波网络的性能，利用小波网络的非线性函数逼近能力，对非线性静态系统和非线性动态系统进行辨识。
The properties of the wavelet networks are analyzed. According to the approximation ability of wavelet networks, the nonlinear static system and the nonlinear dynamic system can be identified.
分析了小波网络的性能，利用小波网络的非线性函数逼近能力，对非线性静态系统和非线性动态系统进行辨识。
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