Radial Basis Function Network Learning with Modified Backpropagation Algorithm

Abstract

Radial Basis Function Network (RBFN) is a class of Artificial Neural Network (ANN) that was used in many classification problems in science and engineering. Backpropagation (BP) algorithm is a learning algorithm that was widely used in ANN. However, BP has major disadvantages of slow error rate convergence and always easily stuck at the local minima. Hence, Modified BP algorithm was proposed in this study to improve the learning speed of RBFN using discretized data. C programming language was used to develop the program for the proposed method. Performance measurement of the method was conducted and the experimental results indicate that our proposed method performs better in error rate convergence and correct classification compared to the result with continuous dataset. T-test statistical analysis was used to check the significance of the results and most were found to be satisfactorily significant. DOI: http://dx.doi.org/10.11591/telkomnika.v13i2.7032