Description: In the RBF neural network learning process, I first calculate the F neurons in the input and the distance between the center and then on the distance of a nonlinear transformation. Output layer and hidden layer are different tasks, this is not the same two learning strategies. Linear output layer is the right to adjust, using the linear optimization strategy, thus learning faster. The hidden layer is the transfer function parameters can be adjusted using a nonlinear optimization strategy, and thus learn more slowly. Algorithms use Gaussian function as the RBF hidden layer transfer function from hidden layer to achieve from the x Well R, (x) non-linear mapping from the output layer to transition from R, (X )---> y. Linear mapping.
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BRP源程序.doc
七个RBF神经网络的源程序\RBFFunction.m.txt
.......................\RBFyuce.m.txt
.......................\RBF_cluster聚类.m.txt
.......................\RBF_Gradient梯度.m.txt
.......................\RBF_k.m.txt
.......................\RBF_OLS.m.txt
.......................\RBF建模.m.txt
七个RBF神经网络的源程序
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