Based on the single scaling wavelet frame theory and radial basis function neural network, a multi dimensional input and output wavelet network is constructed.
在探索单尺度径向小波框架与径向基函数网络对函数逼近特性相似的基础上，构造了单尺度径向基小波网络。
The system includes the following nine modules: pretreatment, wavelet analysis, spectral analysis, forward modeling, inversion, input, output, graph display and help.
系统包括预处理、小波分析、频谱分析、正演、反演、输入、输出、图形显示、帮助等九个模块。
The results show that this algorithm can model input and output learning kernel of dynamic nonlinear system quickly, which is superior to other learning methods of wavelet network.
结果表明该算法能够对动态非线性系统的输入输出快速学习和建模，优于其它小波网络的学习算法。
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