Selection of RBF Neural Network Parameters Based on Normalized Cut Clustering

This paper presents a method for constructing a radial basis function network based on normalized cut clustering for determining center and width of radial basis functions. normalized cut clustering can separate clusters that are non-linearly separable in the input space, so it can be able toconstruct an rbf network classifier with reduced number of hidden layer neurons in comparison with conventional rbf network obtained by k-means method. the well known pseudo inverse method is used to adjust the weights of the output layer of rbf network. quantitative and qualitative evaluations show that the proposed method reduces the number of hidden units and preserves classification accuracy in comparison with conventional rbf network generated by k-means method.