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The Fluorescence Spectroscopy Recognition of the Mineral Oil Based on the Multiresolution Orthogonal Multiwavelet Neural Network

机译:基于多分辨率正交多小波神经网络的矿物油荧光光谱识别

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The singular value eigenvectors of the different kinds of the mineral oil stylebooks are obtained by parameterizing the three-dimensional fluorescence spectroscopy. They are complicated and not easy to be recognized by the simple formula. The multiwavelet neural network is introduced to realize the identification of the different kinds of the mineral oil. It was layered. It had the feature of the part study. The prompting function of the network is constructed by the multiscale function and multiwavelet function. The experiment indicates that the network has all the virtue of the wavelet neural network (WNN). It also has the much better approach property than the WNN. It can effectively recognize the fine distinction between the different spectrums and realize the identification of the oil by much fewer train times than the WNN.
机译:通过对三维荧光光谱进行参数化,可以获得不同种类的矿物油样式簿的奇异值​​特征向量。它们很复杂,不容易用简单的公式识别。引入多小波神经网络实现对不同种类矿物油的识别。它是分层的。它具有部分研究的特征。网络的提示功能由多尺度函数和多小波函数构成。实验表明,该网络具有小波神经网络(WNN)的所有优点。它也具有比WNN更好的方法属性。它可以有效地识别不同频谱之间的精细区别,并通过比WNN少得多的火车时间来实现油的识别。

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