Improved neural network ensemble for the prediction of PMV index

In order to predict the PMV index more effectively on a large sample set, a neural network ensemble method based on fuzzy c-means clustering (FCM) is presented. By using the FCM algorithm, the original sample set is divided into some intersectant subsets, and with these subsets the corresponding individual neural networks can be trained as parts of an integrated system. When putting this system into use, the prediction result can be obtained by summing the products of the individual networks' outputs and weights. However, the method consists mainly of two local optimum algorithms, of which one is the hill climbing method in fuzzy c-means clustering, the other the steepest descent method in training BP neural network. As an effective improved scheme, the particle swarm optimization is introduced to cover the shortage of local optimum. In this way, the performance of the predictive system is greatly promoted. Experiments indicate that the method has a good performance of global optimum in neural network ensemble, and the compositive network system can predict the PMV index more accurately.