群智能算法優(yōu)化SVR預測模型的應用與分析

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群智能算法優(yōu)化SVR預測模型的應用與分析
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作者單位:(內蒙古科技大學(xué) 信息工程學(xué)院,內蒙古 包頭 014010)
作者簡(jiǎn)介:朱 林(1957-),女,河北承德人,教授,碩士研究生導師,主要從事智能控制、工業(yè)遠程控制方向的研究。[FQ)]
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中圖分類(lèi)號:TP301
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Application and Analysis about Optimization of SVR Forecasting Model by Swarm Intelligence Algorithm

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(Information Engineering College, Inner Mongolia University of science and technology, Baotou 014000,China)[JZ)]

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摘要:

群體智能是基于生物群體行為規律的智能計算技術(shù),常用以解決參數尋優(yōu)等問(wèn)題；作為群體智能的兩種典型算法,蟻群算法和粒子群算法應用極為廣泛；文章分析了標準蟻群算法和粒子群算法的不足,分別采用改進(jìn)的蟻群算法和粒子群算法對支持向量機回歸模型參數進(jìn)行優(yōu)化,并以釹鐵硼吸氫階段合金氫含量預測為例,通過(guò)MATLAB對改進(jìn)后的預測模型進(jìn)行了仿真驗證,最終給出了兩種方法優(yōu)化后,模型的預測效果及性能對比；仿真結果表明,改進(jìn)的群體智能算法對工藝優(yōu)化控制有著(zhù)重要的意義。

Abstract:

As a intelligent computing technology based on biological laws of group behavior,Swarm intelligence is widely used to solve the problems of parameter optimization. Ant colony algorithm and particle swarm optimization are widely used as two typical algorithms of swarm intelligence. In this paper we did analyze the insignificance of the standard ant colony algorithm and particle swarm optimization,then the improved ant colony algorithm and particle swarm optimization were respectively used to optimize the parameters of the regression model of support vector machine,and the hydrogen content of NdFeB alloy in the hydrogen absorption stage is taken as an example to simulates and verifies the improved model by MATLAB. The contrast of prediction performance of the regression model between two algorithms was given at last. The results of simulation indicate that the improved swarm intelligence algorithm has important significance on optimizing the process control.

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朱林,陸春偉.群智能算法優(yōu)化SVR預測模型的應用與分析計算機測量與控制[J].,2014,22(9):2890-2892.

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收稿日期:2014-05-29
最后修改日期:2014-06-30
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在線(xiàn)發(fā)布日期: 2014-12-18
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