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Opposite Degree Computation And Its Application

Author

Listed:
  • Xiao Guang Yue

    (School of Civil Engineering, Wuhan University, Wuhan, China)

  • Muhammad Aqeel Ashraf

    (School of Environment, China University of Geosciences, Wuhan, China)

Abstract

In order to predict numerical value, we propose a new intelligent algorithm opposite degree computation algorithm. The opposite degree computation algorithm is based on the degree of antagonism between the data to analyze the approximate relationship. The experiment was conducted at Chinese Xinjiang Province, during year 1995 to year 2010. Opposite degree computation algorithm is based on priori value, posteriori value, priori matrix, posterior matrix and the relationship between calculation data. By learning Chinese Xinjiang cotton production data from 1995 – 2005, forecasts 2006 – 2010 cotton production; the result of the absolute error is 9.3237%. Meanwhile, we introduce the prediction method based on BP neural network for the result comparison and found opposite degree computation method is superior to the BP neural network method. Cotton production prediction based on opposite degree computation proved the algorithm is feasible and effective and can be used in numerical value prediction.

Suggested Citation

  • Xiao Guang Yue & Muhammad Aqeel Ashraf, 2018. "Opposite Degree Computation And Its Application ," Engineering Heritage Journal (GWK), Zibeline International Publishing, vol. 2(1), pages 5-13, January.
    • Handle: RePEc:zib:zbngwk:v:2:y:2018:i:1:p:5-13
    DOI: 10.26480/gwk.01.2018.05.13
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    References listed on IDEAS

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    1. Zhou, Zu-liang & Yin, Chun-wu, 2011. "Application of Gray Metabolic Model in the Prediction of the Cotton Output in China," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 3(01), pages 1-3, January.
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