Research Of Neural Network Ensemble And Its Application To Classification And Regression

Neural network ensemble can significantly improve generalization of neural network systems by training several neural networks simply and combining their results. The research of ensemble learning is focused on two aspects, namely, how to combine the results of multiple neural networks and how to generate individual neural network, including how to effectively use the limited sample set. Selective ensemble can effectively reduce the generalization error of ensemble learning. In this paper, against inadequacy of existing methods, a dynamic selective ensemble method and a kernel fuzzy c-means clustering ensemble method are proposed which combined kernel principal components analysis for features extraction of high dimension fault data to further improve the accuracy and stability of classification for fault diagnosis of steam turbine. Lastly, an entropy weighted ensemble method is proposed to further improve the accuracy of prediction for wind speed.