document: Optimal learning for patterns classification in RBF networks The proposed modifying of the structure of the radial basis function -LRB- RBF -RRB- network by introducing the weight matrix to the input layer -LRB- in contrast to the direct connection of the input to the hidden layer of a conventional RBF -RRB- so that the training space in the RBF network is adaptively separated by the resultant decision boundaries and class regions is reported . The training of this weight matrix is carried out as for a single-layer perceptron together with the clustering process . In this way the network is capable of dealing with complicated problems , which have a high degree of interference in the training data , and achieves a higher classification rate over the current classifiers using RBF
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