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Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm

Author

Listed:
  • Jaber Soltani
  • Moosa Kalanaki
  • Mohammad Soltani

Abstract

This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.

Suggested Citation

  • Jaber Soltani & Moosa Kalanaki & Mohammad Soltani, 2016. "Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm," Modern Applied Science, Canadian Center of Science and Education, vol. 10(7), pages 1-29, July.
    • Handle: RePEc:ibn:masjnl:v:10:y:2016:i:7:p:29
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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