Modelling power output at nuclear power plant by neural networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
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When we manipulate high dimensional data with Elman neural network, many characteristic variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the the accuracy of recognition finally. PCA,which is short for principal component analysis is a multivariate analysis method which transforms many characteristic variables to few synthetic variables. Not only can it eliminate relationship among characteristic variables but new variables can also hold most information of the original ones as well. In this paper we make full use of the advantages of PCA and the properties of Elman neural network structures to establish PCA-Elman based on PCA. The new algorithm reduces dimensions of the high dimensional data by PCA, and carry on network training and simulation with low dimensional data that we get, which obviously simplifies the network structure, and in the process, improves the training speed and generalization capacity of the Elman neural network. We prove the effectiveness of the new algorithm by case analysis. The algorithm improves the efficiency in network problems solving and is worth further generalizing.