| 基于改进的PSO-SVM法的大坝安全非线性预警模型研究 |
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| 引用本文： | 范振东,崔伟杰,郭芝韵,张毅. 基于改进的PSO-SVM法的大坝安全非线性预警模型研究[J]. 水电能源科学, 2014, 32(11): 72-75 |
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| 作者姓名： | 范振东 崔伟杰 郭芝韵 张毅 |
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| 作者单位： | 河海大学 水利水电学院， 江苏 南京 210098；河海大学 水文水资源与水利工程科学国家重点实验室， 江苏 南京 210098;雅砻江流域水电开发有限公司， 四川 成都 610051;河海大学 大禹学院， 江苏 南京 210098;黄河上游水电开发有限责任公司， 青海 西宁 810008 |
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| 基金项目： | 高等学校博士学科点专项科研基金资助课题（20130094110010）；江苏省杰出青年基金（BK2012036）；国家自然科学基金项目（51179066）；水利部公益性行业科研专项经费项目（201301061） |
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| 摘 要： | 针对常用的大坝安全预警模型存在的不足,将改进的粒子群(PSO)算法与支持向量机(SVM)相结合,建立了基于改进的PSO-SVM法的大坝安全非线性预警模型,即利用粒子群算法对支持向量机模型的参数进行寻优,同时为防止粒子群寻优过程陷入局部最优点,引入了位置因子和速度因子,并通过实例应用做了比较。结果表明,改进后的模型有效摆脱了粒子群陷入局部最优点,且具有更好的非线性拟合能力和泛化能力,可用于复杂大坝安全非线性预警建模。
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| 关 键 词： | 大坝安全； 预警模型； 支持向量机； 粒子群算法 |
Nonlinear Early-warning Model of Dam Safety Based on Improved PSO-SVM |
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| FAN Zhendong,CUI Weijie,GUO Zhiyun and ZHANG Yi. Nonlinear Early-warning Model of Dam Safety Based on Improved PSO-SVM[J]. International Journal Hydroelectric Energy, 2014, 32(11): 72-75 |
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| Authors: | FAN Zhendong CUI Weijie GUO Zhiyun ZHANG Yi |
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| Affiliation: | College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;Yalong River Hydropower Development Company Limited, Chengdu 610051, China;Dayu College, Hohai University, Nanjing 210098, China;Yellow River Upstream Hydropower Development Co. Ltd., Xining 810008, China |
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| Abstract: | Aiming at the drawbacks of traditional early-warning model of dam safety, combination of improved PSO and SVM is used to establish nonlinear early-warning model. The parameters of SVM are optimized with PSO. At the same time, position and velocity factors is introduced to PSO for escaping local optimal point. Finally, case example is comparative analysis. The results show that an improved model can effectively overcome local optimal point and it has better nonlinear fitting and generalization ability. Therefore, it is more suitable for modeling nonlinear early-warning model of complex dam safety. |
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| Keywords: | dam safety early-warning model support vector machine particle swarm optimization |
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