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RBF_per

于 2021-01-12 发布文件大小:15KB

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先归一化输入输出参数，用KMens优化RBF神经网络中心值，然后计算隐节点数据中心间的距离（矩阵），得到各隐节点的扩展常数（宽度），接着用最小二乘法得到各隐节点的输出权值，对数据进行反归一化，并绘图得到神经网络输出与测试集图，同时进行性能评价(First, the input and output parameters are normalized, the central value of the RBF neural network is optimized by KMens, then the distance (matrix) between the data centers of the hidden nodes is calculated, and the extended constant (width) of the hidden nodes is obtained. Then the output weights of the hidden nodes are obtained by the least square method, and the data are back normalized, and the neural network is drawn. Output and test set diagrams, and performance evaluation)

文件列表：

seeds_dataset.data, 59999 , 2018-06-14
DataImport5.m, 2430 , 2018-06-21
Kmens01.m, 4249 , 2018-06-27

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