基于改进粒子群优化LSSVM的金属腐蚀速率预测模型

Prediction Model of Metal Corrosion Rate Based on Improved Particle Swarm Optimizing LSSVM

针对电力金属设施在土壤中的腐蚀预测问题,分析现有腐蚀预测方法的不足,考虑金属腐蚀影响因素,研究提出了一种采用改进粒子群优化LSSVM的金属腐蚀速率预测方法。在传统粒子群算法中引入收缩因子,以控制粒子速度,增强粒子的搜索能力,从而解决粒子群早熟问题。采用实验数据进行仿真分析,改进PSO-LSSVM预测模型的平均相对误差仅为2.24%,与其他几种方法相比,改进粒子群优化的LSSVM算法具有更高的预测精度。

In view of the corrosion prediction of the electric metal facilities in the soil,the deficiencies of the existing corrosion prediction methods are analyzed,and the factors affecting the metal corrosion are considered,and a prediction method of metal corrosion rate using improved particle swarm optimizing LSSVM is proposed.The shrinkage factor is introduced into the traditional particle swarm optimization algorithm to control particle velocity and enhance search ability,so as to solve the premature problem of particle swarm optimization.The average relative error of the improved PSO-LSSVM prediction model is only 2.24%.Compared with several other methods,the improved particle swarm optimizing LSSVM algorithm has higher prediction accuracy than other methods.