By the family of wavelet basis function of wavelet Composition, it can describe the signal time (space) and frequency (scale) domain of the local characteristics.
小波由一族小波基函数构成，它可以描述信号时间(空间)和频率(尺度)域的局部特性。
This fuzzy neural network USES wavelet basis function as membership function whose shape can be adjusted on line so that the networks have better learning and adaptive ability.
这种模糊神经网络利用了小波基函数作为隶属函数，可在线根据误差调整隶属函数的形状，使模糊神经网络具有更强的学习和适应能力。
The practical problems in the fault detection and fault location using the wavelet transform, such as the wavelet basis function, the initial data, the velocity of traveling wave are studied.
针对小波变换在故障检测与测距中应用的实际问题如小波基函数、小波变换初始数据、行波波速的选取等进行了研究。
On the basis of evaluation function of using image intensity variance, we proposed a new auto-focusing algorithm by means of Wavelet Transform Multi-Resolution Analysis(WTMRA) theory.
在传统图像灰度方差评价函数的基础上，利用小波多分辨率分析，提出了一种新的自动聚焦算法。
This paper discuss the effects of the wavelet function and the decomposed level on remote fused image deeply. The research provides basis for choosing wavelet function and the decomposed level.
详细地探讨了小波基函数、小波分解层数的选取对遥感图像融合结果的影响，为小波基函数和分解层数的选择提供了依据。
Wavelet neural networks can be regard as not only the function-linked networks based wavelet function, but also the extension of Radical basis function (RBF) networks.
小波神经网络可以看作是以小波函数为基底的一种函数连接型网络，也可以认为是径向基函数(RBF)网络的推广。
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。
Soft Thresholding Wavelet-based Radial Basis Function Neural network (STWRBFN) method was developed to perform simultaneous quantitative analysis of multicomponent mixtures.
建立了一种小波软阈值径向基函数神经网络(STWRBFN)方法，同时定量分析了多组分混合物。
Firstly, the best wavelet basis selection was conducted via message entropy function; then the gas load was decomposed to low frequency signal and high frequency signal by two times through it.
首先用信息熵函数最小选择最优小波基，然后用其对燃气负荷进行二层分解得到负荷的低频信号和高频信号。
A novel pattern recognition method based on wavelet packet analysis and radial basis function network is presented in this paper.
论文给出了改进型径向基网络应用示例，验证了改进型径向基网络的函数实现功能和模式分类功能。
A novel pattern recognition method based on wavelet packet analysis and radial basis function network is presented in this paper.
论文给出了改进型径向基网络应用示例，验证了改进型径向基网络的函数实现功能和模式分类功能。
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