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International Conference on Intelligent Computing and Information Science

ICICIS 2011: Intelligent Computing and Information Science pp 130–135Cite as

GA-Based Optimization Approach of Fractional Linear Neural Network and Its Application

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 134))

Abstract

A GA-based optimization method of fractional linear neural network (FNN) is proposed. Firstly, the GA is used to optimize the weight of fractional linear neural network. A solution near the global optimum will be found. Then the global optimum in the local area can be obtained with back propagation algorithm for the FNN to train the network based on the solution. The simulation results show that the GA-based new approach to optimize the fractional neural network is feasible and effective.

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References

  1. Zhang, S., Zhang, X., Wang, L., Ding, J., Liu, G.: Study on annual precipitation and runoff forecast based on artificial neural network in the upper reach of yellow river. China Rural Water and Hydropower 1, 41–44 (2005)

    Google Scholar 

  2. Wu, Q., Zhang, Q., Wa, J.: Application of ANN in weather forecast. Computer Engineering 31, 176–179 (2005)

    Google Scholar 

  3. Yang, G., Wang, S., Yan, Q.: Research of fractional linear neural network and its ability for nonlinear approach. Chinese Journal of computers 30(2), 189–199 (2007)

    Google Scholar 

  4. Goldberg, D.E.: Genetic algorithm in search optimization and machine learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  5. Goldberg, D.E.: The design of innovation. Kluwer Academic Publishers, Massachusetts (2002)

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Information Engineering, Nanchang Hangkong University, 330063, Nanchang, China

    Guowei Yang

  2. School of Instrumentation Science and Opto-electronics Engineering, BeiHang University, 100191, Beijing, China

    Lei Guo

Authors
  1. Guowei Yang
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  2. Lei Guo
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Editor information

Editors and Affiliations

  1. The Key Laboratory of Manufacture and Test, Chongqing University of Technology, 400054, Chongqing, P. R. China

    Ran Chen

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© 2011 Springer-Verlag Berlin Heidelberg

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Cite this paper

Yang, G., Guo, L. (2011). GA-Based Optimization Approach of Fractional Linear Neural Network and Its Application. In: Chen, R. (eds) Intelligent Computing and Information Science. ICICIS 2011. Communications in Computer and Information Science, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18129-0_21

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  • DOI: https://doi.org/10.1007/978-3-642-18129-0_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18128-3

  • Online ISBN: 978-3-642-18129-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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