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


  •  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.


This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1913-1844
  • Issn(Onlne): 1913-1852
  • Started: 2007
  • Frequency: monthly

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(The data was calculated based on Google Scholar Citations)

Google-based Impact Factor (2018): 6.49

h-index (January 2018): 30

i10-index (January 2018): 163

h5-index (January 2018): 19

h5-median(January 2018): 25

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