A PERFORMANCE EVALUATION OF PRUNING EFFECTS ON HYBRID NEURAL NETWORK

In this paper, we explore the pruning effects on a hybrid mode sequential learning algorithm namely FuzzyARTMAP-prunable Radial Basis Function (FAM-PRBF) that utilizes Fuzzy ARTMAP to learn a training dataset and Radial Basis Function Network (RBFN) to perform regression and classification. The pruning algorithm is used to optimize the hidden layer of the RBFN. The experimental results show that FAM-PRBF has successfully reduced the complexity and computation time of the neural network.