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Research of Wavelet Neural Network Model Based on Extenics

机译:基于兴奋的小波神经网络模型研究

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In order to conquer the disadvantage of the traditional Wavelet Neural Networks (WNN), the paper presents WNN model which is based on extenics. The model uses the feature number of matter element to determine the volume of importation of neuron number, makes sure the number of output neurons based on identification the type number needed, and makes a correct judgment about the neurons inhibit or activation. It optimizes the structure design of wavelet neural network. Then there is an experiment about the weather prediction using the new model. Experimental results show that the new model has better convergence and accuracy.
机译:为了征服传统小波神经网络(WNN)的缺点,本文提出了基于延长的WNN模型。该模型使用物质元素的特征数量来确定神经元数的进口量,确保基于鉴定所需的类型数的输出神经元数,并对神经元抑制或激活作出正确的判断。它优化了小波神经网络的结构设计。然后有关于使用新模型的天气预报的实验。实验结果表明,新模型具有更好的收敛性和准确性。

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