Modular adaptive RBF-type neural networks for letter recognition

This paper proposes a modular RBF-type network classifier consisting of multiple single-layer RBF networks and perceptrons for solving large-sample and high-dimensional classification problems. The method for optimally determining the number, locations and widths of RBF kernels and the target values of Gaussian activation functions is gone into details. The presented method, which only propagates errors one layer backwards, has much lower computational complexity than the backpropogation algorithm used in multilayer feedforward perceptrons and RBF networks. The result for letter recognition shows that modular RBF-type network classifiers as well as the adaptively learning algorithm have faster convergence rate, higher classification accuracy, larger probability to get optimal structures, and better performance to reach global minimum points, compared with the standard RBF networks and multilayer feedforward perceptrons.