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。
Based on the learning characteristic of neural network and the function approximation ability of the wavelet, a new self tuning control algorithm is presented.
依据小波的非线性逼近能力和神经网络的自学习特性，提出了一种基于小波神经网络模型的自校正控制算法。
The algorithm improves the EZW algorithm through inducting fast successive approximation quantization and effective treatment of marginal subband of wavelet transform.
该方法通过引入快速逐次逼近量化和对小波变换边缘子带的特殊处理，对EZW算法进行了改进。
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