A Comparison of Radial Basis Probabilistic Neural Network and Radial Basis Function Neural Network Performance Based on Sensitivity Analysis

Abstract

This paper presents a comparative study of the performance learning algorithm for Radial Basis Probabilistic Neural Network (RBPNN), and the Radial Basis Function Neural Network (RBFNN), are evaluated and compared for their ability to classify data based on sensitivity analysis. RBPNN generally performs similarly to RBFNN. Both of them are trained using gradient descent. In this research, sensitivity analysis is used to prune the feature data. The results show that the network still works well after pruning. The issues of network optimization and computational efficiency in use are discussed. Finally, to evaluate the performance, our experiments are demonstrated by two examples of real life data set.