Prediction model based on PCA - DRKM-RBF

Abstract

This paper presents a new neural network predictive model named PCA - DRKM - RBF, which combines Principal Component Analysis (PCA), Dynamic Rough set K-means (DRKM) and Radial Basis Function (RBF) neural network. The data processed by the principal component analysis is the neural network's input, and the RBF neural network's hidden nodes are the centers using DRKM. The paper forecasts the cyclodextrin closure constant, and the results indicate that the model has obviously improved the accuracy of prediction. © 2013 Springer-Verlag GmbH.