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Predicting Gas Emission Based on Combination of Grey Relational Analysis and Improved Fuzzy Neural Network

  • Xiaoyan Tang
  • Zhengguo Wang
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 169)

Abstract

Gas emission is controlled by various factors; it is a very complex problem how to predict gas emission according to these factors. When by using geological information and winning technical data to predict it, there are lots of uncertainty, ambiguity and highly nonlinear characteristics. This feature is difficult using traditional mathematical expressions to describe. This paper combines grey relational analysis with improved fuzzy neural network (IFNN) to predict gas emission. Based on grey relational degree these gas emission-sensitive factors are optimality collected. Take these factors as input of IFNN model, the prediction model of gas emission is established. Results show that it is reliable. Recognition precision is high, and practicability is better.

Keywords

Grey Relation Improved Fuzzy Neural Network Gas Emission Prediction 

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References

  1. 1.
    Shitong, W.: Fuzzy Neural System, Fuzzy Neural Network and Application Design. Shanghai Science and Technology Literature Publishing House, Shanghai (1998)Google Scholar
  2. 2.
    Shaohua, X., Jiuzhen, L.: Study on Pattern Selection and Generalization Ability for Neural Networks. Computer Science 28(6), 94–96 (2001)Google Scholar
  3. 3.
    Lixin, W.: Fuzzy Systems and Fuzzy Control Tutorial. Tsinghua University Press, Beijing (2003)Google Scholar
  4. 4.
    Songmei, Z., Ji, W., Feng, W., et al.: Grain Yield Prediction Model of Liaoning Province Based on Grey Relational Analysis. Water Saving Irrigation (5), 64–66 (2011)Google Scholar
  5. 5.
    Tang, X., Liu, Z.: Igneous Rocks Recognition Based on Improved Fuzzy Neural Network. In: Shen, G., Huang, X. (eds.) CSIE 2011, Part II. CCIS, vol. 153, pp. 338–342. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of Geology and EnvironmentXi’an University of Science and TechnologyXi’anChina
  2. 2.Trans-Asia Gas Pipeline Company LimitedBeijingChina

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