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Based on support vector machine(SVM)and relevance vector machine(RVM)theory,the parameters are optimized through adaptive particle swarm algorithm(APPSO)to improve fitting accuracy and generalization ability of dam safety warning model.So,we set up dam safety warning model based APPSO-SVM and APPSO-RVM.Compared SVM with RVM,example's results indicate that although the number of relevance vector of APPSO-RVM model is less than APPSO-SVM model,the fitting accuracy and generalization ability of APPSO-RVM is better than APPSO-SVM model.Therefore,we should choose APPSO-RVM model in reality.
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Basic Information:
DOI：10.20040/j.cnki.1000-7709.2015.03.012
China Classification Code:TV698.1
Citation Information:
[1]DU Chuan-yang,ZHENG Dong-jian,CHEN Min ,et al.Application and Comparison of SVM and RVM Algorithm in Dam Safety Modeling[J].Water Resources and Power,2015,33(03):45-49.DOI:10.20040/j.cnki.1000-7709.2015.03.012.
Fund Information:
国家自然科学基金重点项目(41323001,51139001); 国家自然科学基金项目(51379068,51179066,51279052,51209077); 高等学校博士学科点专项科研基金(20120094110005,20120094130003,20130094110010); 新世纪优秀人才支持计划资助项目(NCET-11-0628,NCET-10-0359); 水利部公益性行业科研专项经费项目(201201038,201301061); 江苏省杰出青年基金项目(BK2012036); 江苏省第四期“333工程”培养资金资助项目(BRA2011179,BRA2011145); 江苏高校优势学科建设工程资助项目(水利工程)(YS11001); 江苏省“333高层次人才培养工程”项目(2017-B08037); 江苏省“六大人才高峰”项目(JY-008); 江苏省“333高层次人才培养工程”科研项目(2016-B1307101)
2015-03-25
2015-03-25