A list,

RBF neural network is a three-layer neural network, which includes input layer, hidden layer and output layer. The transformation from input space to hidden space is nonlinear, while the transformation from hidden space to output space is linear. The flow diagram is as follows:



The basic idea of RBF network is that RBF is used as the “basis” of the hidden element to form the hidden layer space, so that the input vector can be directly mapped to the hidden space without weight connection. When the center point of RBF is determined, the mapping relationship is determined. The mapping from the hidden layer space to the output space is linear, that is, the output of the network is the linear weighted sum of the output of the hidden element, where the weight is the adjustable parameter of the network. Among them, the function of the hidden layer is to map the vector from the lower dimension P to the higher dimension H, so that the case of the lower dimension linearly indivisible can become linearly separable to the higher dimension, mainly the idea of kernel function. Thus, the network mapping from input to output is nonlinear, while the network output is linear with respect to tunable parameters. The weights of the network can be solved directly by linear equations, which greatly speeds up learning and avoids local minimum problems.



2. RBF neural network learning problem

Ii. Source code

%% I. Clear all CLC %% II. Training set/test set generates %% %1.Import datalist=dir(['.\data\'.'*.xls']);
k=length(list); data_train_x=[]; % Training set input data_train_y=[]; % training set output data_test_x=[]; % test set input data_test_y=[]; % test set outputfor i=1:k
 str= strcat ('.\data\'.list(i).name)
ifi<k [data_train_x1,data_train_y1]=data_chuli(str); data_train_x=[data_train_x;data_train_x1]; % Training set input data_train_y=[data_train_y;data_train_y1]; % Training set inputelse[data_test_x1,data_test_y1]=data_chuli(str); data_test_x=[data_test_x;data_test_x1]; % training set input data_test_y=[data_test_y;data_test_y1]; % training set enter end endCopy the code

3. Operation results

Fourth, note

Version: 2014 a