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首页> 外文期刊>International Journal of Information and Communication Technology >The PSO optimisation SVM prediction model for the asphalt pavement environment and service fatigue life
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The PSO optimisation SVM prediction model for the asphalt pavement environment and service fatigue life

机译：The PSO optimisation SVM prediction model for the asphalt pavement environment and service fatigue life

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摘要

In order to improve the accuracy of prediction by support vector machine (SVM), parameter optimisation of SVM is an important part of asphalt pavement life prediction. In this paper, a particle swarm optimisation support vector machine (PSO_SVM) method was proposed to predict the fatigue life of SBS modified asphalt mixture. This method combines SVM with particle swarm optimisation (PSO), makes full use of SVM's unique advantages in dealing with small sample regression problems and PSO global search optimisation, improves convergence speed, and achieves depth and breadth optimisation. Experimental results show that this method improves the parameter selection efficiency of SVM, and the prediction results are more accurate than those of ANN and SVM.

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