Initialization of Radial Basis Function Neural Networks with Prior Domain Information for Good Generalization
نویسنده
چکیده
The effect of initialization of Radial Basis Function (RBF) Neural Network (NN) with prior domain information is determining for generalization ability of the network. It defines the number of hidden units in a hidden layer in advance and minimizes the time of learning. The paper describes how to create RBF NN simulator including prior domain information and how the initialization works on the final result. A task for technical diagnostics of objects with fuzzy domain is examined by moving casual centers of clusters and moving domain centers and widths.
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