The Research of Negative Correlation Learning Based on Artificial Neural Network

Authors:
Yi Ding;Xufu Peng;Xian Fu
Affiliations:
Department of Computer Science and Technology, Hubei Normal University, Huangshi, China 435002 and Department of Computer Science and Technology, Huazhong University of Science and Technology, Wuh ...;Department of Computer Science and Technology, Hubei Normal University, Huangshi, China 435002;Department of Computer Science and Technology, Hubei Normal University, Huangshi, China 435002
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

The integrated technology of the artificial neural network is a research focus of the neural computing technology, which possesses ripe applications in a lot of fields. The neural network ensemble studies the same question with limited neural networks. The output of the ensemble under some input example is determined by all the output of the neural network forming the ensemble under the same input example. The negative correlation learning, which encourages different individual network to study and train different parts of the ensemble in order to make the whole ensemble study the whole training data better, is a training method for the neural network ensemble in this paper. Using a BP algorithm with impulse in the error function is an improvement of the method of negative correlation learning in the paper. The method is an algorithm in batches with more powerful generalization ability and studying of speed, because it combines primitive correlation learning with BP algorithm of impulse.