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应用连续小波变换对脑机接口（BCI）信号处理，进入神经网络分类，包括BP网络和LVQ网络-continuous wavelet transform application of brain-machine interfaces (BCI) signal processing, neural network access classification, BP network including network and LVQ
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本文提出了一种自动检测和电能质量扰动分类模糊专家系统。有关干扰的类型包括电压骤降，骤升，中断，切换瞬变，冲动，闪烁，谐波和缺口。据推测，在所分析的波形采样的形式提供。傅立叶变换和小波分析，利用独特的功能获得的波形一个模糊专家系统为主体的决定有关的干扰类型而设计的。仿真研究，验证了该方法的比较研究人工神经网络为基础的分类方法和该方法研究的准确性也报告，以显示该方法的优点-This paper presents a fuzzy-expert system for automated detectio
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小波神经网络用于分类的基础源码,希望对大家有用，可以用来发表论文，-Wavelet neural network based classification of source code, we hope to be useful, can be used to published papers, thank you
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Wavelet analysis is used as a pre processor for a back-propagation neural network with conjugate gradient learning. The inputs to the neural network are the wavelet maxima neighborhood coefficients of face images at a particular scale. The output of
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小波神经网络中用于分类的代码，代码比较复杂，需要多加学习。(The code used for classification in the wavelet neural network is more complex and needs more learning.)
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