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首页> 外文期刊>Journal of Central South University of Technology >A new support vector machine optimized by improved particle swarm optimization and its application
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A new support vector machine optimized by improved particle swarm optimization and its application

机译：改进粒子群算法优化的新型支持向量机及其应用

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摘要

A new support vector machine (SVM) optimized by an improved particle swarm optimization (PSO) combined with simulated annealing algorithm (SA) was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization(SAPSO) was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improved particle swarm optimization algorithm was used to optimize the parameters of SVM (c, σ and ε). Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18% , respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.
机译：提出了一种通过改进的粒子群算法（PSO）和模拟退火算法（SA）相结合的支持向量机（SVM）。结合模拟退火算法，提高了粒子群优化算法的全局搜索能力，研究了粒子群优化算法的搜索能力。然后，使用改进的粒子群算法对SVM的参数（c，σ和ε）进行优化。基于华北地区电网提供的运行数据，该方法被用于实际的短期负荷预测中。结果表明，与PSO-SVM和传统SVM相比，该方法在实验过程中的平均时间减少了11.6 s和31.1 s，并且该方法的精度分别提高了1.24％和3.18％。 。因此，改进的方法优于PSO-SVM和传统的SVM。

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