| 应用遗传算法优化支持向量机的疲劳裂纹扩展预测 |
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| 引用本文： | 龚兰芳,张昱.应用遗传算法优化支持向量机的疲劳裂纹扩展预测[J].现代制造工程,2011(6). |
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| 作者姓名： | 龚兰芳 张昱 |
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| 作者单位： | 1. 广东水利电力职业技术学院,广州,510635
2. 广东省科学院自动化工程研制中心,广州,510070 |
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| 摘 要： | 准确讯速地预测疲劳裂纹的扩展进程具有十分重要的现实意义和显著的经济效益.为了实现疲劳裂纹长度的准确预测,提出基于遗传算法优化支持向量机(GA-SVM)的疲劳裂纹扩展预测方法,其中遗传算法用于确定SVM中的训练参数,得到优化的SVM预测模型.试验结果表明:用GA-SVM对疲劳裂纹长度进行预测具有很好的预测精度.
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| 关 键 词： | 疲劳裂纹扩展 支持向量机 遗传算法 参数优化 |
Prediction of fatigue crack propagation based on support vector machine optimized by genetic algorithm |
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| GONG Lan-fang,ZHANG Yu.Prediction of fatigue crack propagation based on support vector machine optimized by genetic algorithm[J].Modern Manufacturing Engineering,2011(6). |
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| Authors: | GONG Lan-fang ZHANG Yu |
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| Affiliation: | GONG Lan-fang1,ZHANG Yu2(1 Guangdong Technical College of Water Resource and Electric Engineering,Guangzhou 510635,China,2 Automation Engineering R&M Center,Guangdong Academy of Sciences,Guangzhou 510070,China) |
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| Abstract: | Accurately and rapidly forecasting the fatigue crack propagation is of practical significance and remarkable economic benefit.To forecast fatigue crack propagation exactly,Support Vector Machine optimized by Genetic Algorithm(GA-SVM) is proposed.Genetic Algorithm(GA) is used to determine training parameters of support vector machine in this model,which can gain optimized SVM forecasting model.The experimental results indicate that the proposed GA-SVM model can achieve great accuracy in fatigue crack propaga... |
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| Keywords: | fatigue crack propagation Support Vector Machine(SVM) Genetic Algorithm(GA) parameter optimization |
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