A Modified RBF Neural Network in Pattern Recognition

This paper presents a modified radial basis function (RBF) neural network for pattern recognition problems, which uses a hybrid learning algorithm to adaptively adjust the structure of the network. Two strategies are used to attain the compromise between the network complexity and accuracy, one is a modified "novelty" condition to create a new neuron in the hidden layer; the other is a pruning technique to remove redundant neurons and corresponding connections. To verify the performance of the modified network, two pattern recognition simulations are completed. One is a two-class pattern recognition problem, and the other is a real-world problem, internal component recognition in the field of architecture engineering. Simulation results including final hidden neurons, error, and accuracy using the method proposed in this paper are compared with performance of radial basis functional link network, resource allocating network and RBF neural network with generalized competitive learning algorithm. And it can be concluded that the proposed network has more concise architecture, higher classifier accuracy and fewer running time.