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计算机系统应用:2019,28(1):134-139
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基于改进萤火虫寻优支持向量机的PM2.5预测
范文婷, 王晓
(太原科技大学 计算机科学与技术学院, 太原 030024)
PM2.5 Forecasting Based on Improved Firefly Optimization SVM
FAN Wen-Ting, WANG Xiao
(School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China)
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投稿时间:2018-05-01    修订日期:2018-05-24
中文摘要: 针对现有PM2.5浓度预测误差较大的问题,提出一种基于改进萤火虫寻优支持向量机的预测模型(IFA-SVM).该模型引入邻域搜索和可变步长策略改进萤火虫算法,利用改进FA对SVM的参数C、εγ寻优,用最优参数SVM模型预测太原市PM2.5值.其中邻域搜索策略能为参数优化提供更多更精确的候选解;可变步长可动态调整算法搜索步长,加速收敛,平衡FA的全局和局部搜索能力.将IFA-SVM预测值与萤火虫算法-支持向量机(FA-SVM)、遗传算法-支持向量机(GA-SVM)、粒子群算法-支持向量机(PSO-SVM)相比较.结果表明较其他方法,IFA-SVM模型对太原市未来一天和三天的PM2.5值都取得了更精确的预测性能.
Abstract:Aiming at the problem of large deviation in existing PM2.5 concentration prediction, a novel model based on Improved Firefly Algorithm optimization SVM (IFA-SVM) was proposed. In this model, two neighborhood search strategies and variable step size mechanism were employed to improve FA. The IFA was applied to optimize the SVM parameters (C,, and), and an outstanding model was constructed to forecast PM2.5 concentrations in Taiyuan. The neighborhood search strategies can provide better candidate solutions; search step size was dynamically tuned by using variable step size strategy to accelerate convergence and obtain a trade-off between exploration and exploitation. The performance of the proposed IFA-SVM model has been compared with FA-SVM, Genetic Algorithm (GA)-SVM, and Particle Swarm Optimization (PSO)-SVM. Experimental results show that the proposed IFA-SVM model has achieved more accurate performance for PM2.5 forecasts in 1 day ahead and 3 days ahead compared to other method.
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基金项目:太原科技大学博士基金(20152044)
引用文本:
范文婷,王晓.基于改进萤火虫寻优支持向量机的PM2.5预测.计算机系统应用,2019,28(1):134-139
FAN Wen-Ting,WANG Xiao.PM2.5 Forecasting Based on Improved Firefly Optimization SVM.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):134-139

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