The Research of Artificial Neural Network on Negative Correlation Learning

Authors:
Yi Ding;Xufu Peng;Xian Fu
Affiliations:
The Department of Computer Science and Technology, Huazhong Science and Technology University, Wuhan, China 430072 and The College of Computer Science and Technology, Hubei Normal University,Email ...;The College of Computer Science and Technology, Hubei Normal University,Email: a_carrie@sina.com, Huangshi, China 435000;The College of Computer Science and Technology, Hubei Normal University,Email: a_carrie@sina.com, Huangshi, China 435000
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

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Abstract

An Artificial Neural Network (ANN) is an information processing paradigm inspired by the biological nervous systems. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. The negative correlation learning encourages different individual network to study and trains different parts of the ensemble in order to make the whole ensemble study the whole training data better. This paper improves the method of negative correlation learning by using a BP algorithm with impulse in the error function. The method is an algorithm in batches with more powerful generalization and study speed because it combines primitive correlation learning with BP algorithm of impulse.