A Novel Hybrid Method of Parameters Tuning in Support Vector Regression for Reliability Prediction: Particle Swarm Optimization Combined With Analytical Selection

@article{Zhao2016ANH,
  title={A Novel Hybrid Method of Parameters Tuning in Support Vector Regression for Reliability Prediction: Particle Swarm Optimization Combined With Analytical Selection},
  author={Wei Zhao and Tao Tao and Enrico Zio and Wenbin Wang},
  journal={IEEE Transactions on Reliability},
  year={2016},
  volume={65},
  pages={1393-1405}
}
Support vector regression (SVR) is a widely used technique for reliability prediction. The key issue for high prediction accuracy is the selection of SVR parameters, which is essentially an optimization problem. As one of the most effective evolutionary optimization methods, particle swarm optimization (PSO) has been successfully applied to tune SVR parameters and is shown to perform well. However, the inherent drawbacks of PSO, including slow convergence and local optima, have hindered its… CONTINUE READING

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