SVM :C C RBF [15-21] SVR (3) C g [15] SVR JRC f(xx i )=exp(-g x-x i 2 )g >0 (3) JRC SVR (PSO) [16] SVM SVR ( [17-19] [20] MSE) PSO SVR [21] S

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1 GeologicalScienceandTechnologyInformation Vol.37 No.5 Sep doi: /j.cnki.dzkq [J] (5): ( ;2. ( ) ) : (SVR) SVR 3 - (PSO-SVR) BP (BP-NN) RBF (RBF-NN)2 SVR PSO-SVR : ; ; :TU454 :A : (2018) [1] 3 :1 [2] [9] 4 [10] ; ; BP [12] ; RBF 2 [3-5] RMR Q Gholami [13] (UCS) (RQD) (GSI) [6] ;Shen [7] [8] Copula SVM [14] (Q ) V P E m : : : (2015-K2-008) : (1981 ) @qq.com

2 SVM :C C RBF [15-21] SVR (3) C g [15] SVR JRC f(xx i )=exp(-g x-x i 2 )g >0 (3) JRC SVR (PSO) [16] SVM SVR ( [17-19] [20] MSE) PSO SVR [21] SVR 1 RBF RBF SVR C g SVR [22] SVR PSO SVR Fig.1 FlowchartforPSOoptimizedSVRprediction 1.2 [23] 3 PSO-SVR BP-NN RBF-NN PSO-SVR SVR (1) D Vapnik [14] 1995 SVM : j i : n x i (i)d = x ( j i) y = (αi -α i * )f(xx i )+b (1) x i=12 m (4) i=1 :x j :αiα i * b : 烄 n i=1 (αi -α * i )=0 0 αi 烅 C 0 α * i C 烆 i=12 n (2) : (2) 0.5 j : ξ 1j( Δmin +0.5Δmax i)= (5) Δ1j(i)+0.5Δmax :Δ1j(i) = x 1 (i)-x j (i) ;Δmax = max max j i

3 5 : 277 x 1 (i)-x j (i) ;Δmin=min min x 1 (i)-x j (i) 48 j i : 6 γ1j = m 1 m ξ 1j( i) (6) ( 1) i= ( ) ( ) ( 2) ( ) Barton [24] Q Q [4] ; 20 /(g cm -3 ) 1 Table1 Rockmechanicsparametersofhydropowerdamsite /% /(m s -1 ) /MPa /GPa /GPa : : ; ; ; 2 Table2 Greyrelationalgradeofdiferentfactorwithdeformationmodulusofrockmass /(g cm -3 ) /% /(m s -1 ) /MPa /GPa [25] 0.70;

4 SVR PSO-SVR ( 2) (MSE) (MPE) (R 2 ) 3 ( 4) 4 PSO-SVR 3 RBF SVR (1) 1 BP 2 4 PSO-SVR 315 m 48 PSO-SVR (2) 2 [26] ( 3) R PSO-SVR 20 8 ( ) ( (3) 3 [26] ) PSO-SVR ( ) PSO-SVR MATLAB Table4 Accuracyindicatorsofpredictionmodels RBF R PSO MSE MPE PSO-SVR C g PSO 1 BP-NN MSEC [0.1 RBF-NN ]g [ ] PSO-SVR PSO PSO 2 BP-NN (Cg) RBF-NN ( 3) 3 RBF-NN SVR 3 PSO-SVR BP RBF (1) SVR PSO Table3 ParametersforPSOoptimizedSVR models C g PSO-SVR BP-NN (2) (PSO) (SVR) C RBF g SVR

5 第５期刘超等基于支持向量机回归的岩体变形模量预测７９图３种预测模型变形模量预测值与实测值对比ＦｇＣｍｐｎｂｗｎｍｕｄｎｄｍｄｄｍｎｍｄｕｕｖｕｍｐｎｕｍｂ３ｐｄｃｎｍｄ图３训练样本和测试样本的实测值与ＰＳＯＳＶＲ预测值相关关系图Ｆｇ３Ｃｐｃｎｂｗｎｍｕｄｎｄｐｄｃｄｄｍｎｍｄｕｕｍ３ｃｂｙＰＳＯＳＶＲｍｄ较准确地预测岩体变形模量３何娇王亮清杨国俊基于纵波波速值的岩体质量分级及变形３通过对比分析可以证明ＰＳＯＳＶＲ预测方法的预测精度较高且预测效果优于ＢＰ神经网络黄志鹏谭新水电工程岩体变形模量与波速相关性研究４李维树和ＲＢＦ神经网络预测效果最好５王昌硕梁烨王亮清等基于三维结构面网络模拟的岩体变参考文献１王新峰梁杏刘蕴等裂隙岩体局域离散模型构建及参数敏感性分析以锦屏一级水电站坝区为例Ｊ地质科技情报０１３３６１９５００董学晟水工岩石力学Ｍ北京中国水利水电出版社００４模量估算Ｊ人民长江０１４３增刊１４１６及应用岩石力学与工程学报Ｊ０１０９增刊１７７７３３形模量确定方法Ｊ煤田地质与勘探０１６４４３９７１０６ＰｎｈＳＳｎｇｈＰＫＫｎｈＡＣｍｐｖｕｄｙｈｄｍｎｍｄｕｕｃｋｍＪＢｕｎＥｎｇｎｎｇＧｇｙｈＥｎｖｎｍｎ０１８７７７５１７６０７ＳｈｎＸＣｈｎＭＬｕＷＵｎｇＰｗｖｍｄｕｕｍｈｍｃｈｎｃｍｃｋｍＪＢｕｎｐ

6 EngineeringGeology &theenvironment201676(4):1-10. [8]. Copula Q [J] (8): [J] (3): [9]. [J] (3): [J] (6): [10]. [J]. [J] (2): (4):1-7.. BP [J] (1): [J] (1): [12]. RBF [J] (1): [J] (3): [13]GholamiMBodaghiA.Arobustapproachthroughcombining optimizedneuralnetworkandoptimizedsupportvectorregres- sionfor modelingdeformation modulusofrock masses[j]. ModelingEarthSystems& Environment20173(1):22. [14]VapnikV N.Thenatureofstatisticallearningtheory[J].IEEE TransactionsonNeuralNetworks20028(6): [15] (2): [J] (2): [17]. [18]. PSO-SVM [19]. SVM [20]. [21]. [22]. EMD PSO-SVM [J] (5): [23]. [J]. 1985(2):1. acterisationand tunneldesign[j].internationaljournalof Rock Mechanics& MiningSciences200239(2): [25]. [J]. [26]. PredictionofRockDeformation ModulusBasedon SupportVectorMachineRegression LiuChao 1 TangXibin 1 DengDongmei 2 SunZihao 2 JiangYaofei 2 DengShan 2 (1.GuizhouElectricPowerDesignandResearchInstituteGuiyang550002China; 2.FacultyofEngineeringChinaUniversityofGeosciences(Wuhan)Wuhan430074China) [24]BartonN.Somenew Q-valuecorrelationstoassistin-sitechar- [16]. PSO-SVM [J]. : (11):1904- [J] (5): Abstract:Thedeformation modulusisoneofthe mostimportantparametersforcharacterizationofrock massdeformation.itcanbeobtainedbylaboratoryandfieldexperimentsempiricalrelationmethodnu- mericalsimulationmethodandinteligentnonlinearmethods.inthesemethodstheartificialneuralnet- workmethodisthemostwidelyusedinteligentnonlinearmethodsuchasbackpropagationneuralnet- work (RBF-NN)andradialbasisfunctionneuralnetwork(RBF-NN).Butthestructureofneuralnetwork isdificulttodetermineandthedisadvantageoflocalminimizationandoverlearningleadstolowpredic- tionaccuracy.whilesupportvectormachineregression(svr)methodhasmoreadvantagesinsmalsam- plesandnonlinearpredictionbuttherearefewresearchesinpredictionofrock massmoduluswithsvr methodcurrently.withexperimentaldatafromdaciteofahydropowerstationdamareathispaperselects theoptimalinputvariableusinggreyrelationalanalysis(gra)astheinputvariablesofparticleswarmop- dertorevealitssupremacyofthepso-svr modeloveritscomparisonobjectsi.e.bp-nnrbf-nn modelsin-situtestdataoflongitudinalwavevelocityanddeformation modulusfrom3hydropowersare obtainedtodevelopthecorrespondingpso-svr modelbp-nn modelandrbf-nn model.itturnsout thatthepso-svrpredictionmodelhashigherpredictionaccuracyandismoresuitablefortheprediction ofrockmassdeformation modulus.thesimulationresultsprovedthepresented methodtobeapplicable andtheobtainedresultsconfirmthesuperiorityofthepso-svrin modelingofdeformation modulusof rockmass. timization (PSO)algorithmoptimizedSVR modeltopredictthedeformationmodulusofrockmass.inor- Keywords:rockmassdeformationmodulus;supportvectormachineregression;particleswarmoptimiza- tion;greyrelationalanalysis;longitudinalwavevelocity

供试材料试验试剂和仪器基因组提取分析方法反应体系最佳配比的筛选退火温度对扩增效果的影响

供试材料试验试剂和仪器基因组提取分析方法反应体系最佳配比的筛选退火温度对扩增效果的影响生物技术通报胡立敏陶兴林侯栋朱惠霞张东琴选取温敏花椰菜不育系与杂交组合的高可育和高不育单株构建基因池利用对随机引物对其进行标记同时采用正交设计对其反应体系及扩增条件进行优化试验结果表明在反应体系中含引物