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An Improved Elman Neural Network with Profit Factors and Its Applications

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Intelligent Computing (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4113))

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Abstract

An improved model is proposed by introducing the direction and time profit factors to Elman neural network (NN) and applied to the fields of the finial investing and atmospheric environment. Simulation results show the effectiveness of the proposed model for stock forecasting and the potential of the forecast and assessment for the atmospheric quality.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, 130012, China

    Limin Wang, Xiaohu Shi, Yanchun Liang & Xuming Han

  2. Department of Computer Science and Technology, Changchun Taxation College, Changchun, 130117, China

    Limin Wang

  3. Institute of Information and Spreading Engineering, Changchun University of Technology, Changchun, 130012, China

    Xuming Han

Authors
  1. Limin Wang
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  2. Xiaohu Shi
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  3. Yanchun Liang
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  4. Xuming Han
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China

    De-Shuang Huang

  2. Carnegie Mellon University,  

    Kang Li

  3. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Stranmillis Road, BT9 5AH, Belfast, UK

    George William Irwin

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, L., Shi, X., Liang, Y., Han, X. (2006). An Improved Elman Neural Network with Profit Factors and Its Applications. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_34

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  • DOI: https://doi.org/10.1007/11816157_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37271-4

  • Online ISBN: 978-3-540-37273-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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