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SVR approach based on artificial bee colony optimization

WANG Lin1, ZHANG Yun1, PENG Wen-hui2, XU Bo3, WANG Qian-cheng4   

  1. 1. Department of Airborne Vehicle Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China;
    2. Department of Ballistic Missile and Underwater Weapon, Navy Submarine Academy, Qingdao 266001, China; 
    3. Unit 91467 of the PLA, Jiaozhou 266300, China;
    4. Unit 92635 of the PLA, Qingdao 266001, China
  • Online:2014-02-26 Published:2010-01-03

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Abstract:

To solve the problem that the choice of parameters influence the forecast accuracy of support vector regression (SVR), a SVR forecasting parameters optimization approach based on artificial bee colony algorithm is proposed. The experiment results show that the proposed approach can avoid trapping in the local minimum solution and has the higher forecasting accuracy than the particle swarm optimization algorithm. The approach is applied to the analysis of lubrication metal content time series of airborne vehicle’s powerplant under fault condition, the occurring of fault is forecasted successful.

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