The Research of Artificial Neural Network on Negative Correlation Learning | SpringerLink
Skip to main content
Springer Nature Link
Log in

The Research of Artificial Neural Network on Negative Correlation Learning

  • Conference paper
Advances in Computation and Intelligence (ISICA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5821))

Included in the following conference series:

  • 1377 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hornik, K.M., Stinchcombe, M., Multiayer, H.W.: Feedforward Networks Are Universal Approximators. Neural Networks 2(2), 359–366 (2002)

    Google Scholar 

  2. Liu, Y., Yao, X., Higuchi, T.: Evolutionary Ensembles with Negative Correlation Learning. IEEE Transactions on Evolutionary Computation 4(4), 380–387 (2000)

    Article  Google Scholar 

  3. Rumelhar, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumlhart, D.E., Mcclell, J.L. (eds.), vol. 1(1), pp. 318–362. MIT Press, USA (2001)

    Google Scholar 

  4. Yao, X., Liu, Y.: Making use of population information in evolutionary artificial neural networks. IEEE Transactions on Systems,Man and Cybernetics, Part B: Cybernetics 4(28), 417–425 (1998)

    Google Scholar 

  5. Cooper, L.N.: Hybrid neural network architectures: Equilibrium systems that pay attention. In: Mamone, R.J., Zeevi, Y.Y. (eds.) Neural Network: Theory and Applications, pp. 81–96. Acaddemic Press, CA (1991)

    Google Scholar 

  6. Liu, Y., Yao, X.: Simultaneous training of negatively correlated neural networks in an ensemble. IEEE Transactions on Systems 29(6), 297–310 (1999)

    Google Scholar 

  7. Brown, G.: Diversity in Neural Network Ensembles, PhD thesis, School of Computer Science. University of Birmingham (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Department of Computer Science and Technology, Huazhong Science and Technology University, Wuhan, Hubei, 430072, China

    Yi Ding

  2. The College of Computer Science and Technology, Hubei Normal University,Email: a_carrie@sina.com, Huangshi, Hubei, 435000, China

    Yi Ding, Xufu Peng & Xian Fu

Authors
  1. Yi Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xufu Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xian Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Zhihua Cai

  2. School of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, China

    Zhenhua Li

  3. Computation Center, Wuhan University, 430072, Wuhan, Hubei, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, 965-8580, i, Aizu-Wakamatsu City, Fukushima, Japan

    Yong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ding, Y., Peng, X., Fu, X. (2009). The Research of Artificial Neural Network on Negative Correlation Learning. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04843-2_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04842-5

  • Online ISBN: 978-3-642-04843-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation