matlab解決svr代碼

1、多元線性回歸和BP神經(jīng)網(wǎng)絡(luò)及決策向量機(jī)之間的比較，個(gè)人理解：多元線性回歸：就是多個(gè)屬性的線性組合，在組合時(shí)，通過不斷調(diào)節(jié)每個(gè)屬性的權(quán)重來使多元線性回歸函數(shù)更多的適用于多個(gè)樣本。BP神經(jīng)網(wǎng)絡(luò)：通過使用最快速下降法，通過反向傳播來不斷調(diào)整網(wǎng)絡(luò)中的權(quán)值和閾值，使網(wǎng)絡(luò)的誤差平方和最小。決策向量機(jī)：它仍是對(duì)每個(gè)樣本操作,使得所有樣本距離最終生成的擬合曲線的間隔最小化。算法比較：BP目標(biāo)函數(shù)： J1pmm22j i(yj dj)權(quán)值調(diào)整:wk1決策向量機(jī)目標(biāo)函數(shù)：min1/2wA2支持向量機(jī)（Supportvectormachines,SVM療神經(jīng)網(wǎng)絡(luò)類似，都是學(xué)習(xí)型的機(jī)制，但與神經(jīng)網(wǎng)絡(luò)不同的是SVM使用

2、的是數(shù)學(xué)方法和優(yōu)化技術(shù)。學(xué)習(xí)效率的比較：導(dǎo)入數(shù)據(jù)：File->importdata參數(shù)優(yōu)化常用方法：train_pca,test_pca=pcaForSVM(train_data,test_data,97);函數(shù)為RBF核函數(shù)時(shí)：優(yōu)化前：train_label=data(1:50,1);train_data=data(1:50,2:14);model=svmtrain(train_label,train_data,'-s3-t2-c-g-p');predict_label,mse,dec_value=svmpredict(train_label,train_data,mo

3、del);%±一行利用自身的值和預(yù)測(cè)值進(jìn)行比較，求得模型實(shí)際結(jié)果和預(yù)測(cè)結(jié)果的均方值test_label=data(51:100,1);test_data=data(51:100,2:14);predict_label,mse,dec_value=svmpredict(test_label,test_data,model);優(yōu)化后：train_label=data(1:50,1);train_data=data(1:50,2:14);bestmse,bestc,bestg=SVMcgForRegress(train_label,train_data)%優(yōu)化方法暫定為網(wǎng)格尋優(yōu)cmd=&#

4、39;-c',num2str(bestc),'-g',num2str(bestg),'-s3-t2-p'model=svmtrain(train_label,train_data,cmd);ptrain,mse,dec_value=svmpredict(train_label,train_data,model);figure;%畫圖比較預(yù)測(cè)值和實(shí)際值subplot(2,1,1);plot(train_label,'-o');holdon;plot(ptrain,'r-s');gridon;legend('origi

5、nal','predict');title('TrainSetRegressionPredictbySVM');2 .核函數(shù)為多項(xiàng)式核函數(shù)時(shí)train_label=data(1:50,1);train_data=data(1:50,2:14);bestmse,bestc,bestg=SVMcgForRegress(train_label,train_data);cmd='-c',num2str(bestc),'-g',num2str(bestg),'-s3-t1-p'model=svmtrain(train

6、_label,train_data,cmd);ptrain,mse=svmpredict(train_label,train_data,model);figure;%畫圖比較預(yù)測(cè)值和實(shí)際值subplot(2,1,1);plot(train_label,'-o');holdon;plot(ptrain,'r-s');gridon;legend('original','predict');title('TrainSetRegressionPredictbySVM');Meansquarederror=(regressi

7、on)Squaredcorrelationcoefficient=(regression)3 .核函數(shù)為線性乘積0-linear:u'*vtrain_label=data(1:50,1);train_data=data(1:50,2:14);bestmse,bestc,bestg=SVMcgForRegress(train_label,train_data);cmd='-c',num2str(bestc),'-g',num2str(bestg),'-s3-t0-p'model=svmtrain(train_label,train_data

8、,cmd);ptrain,mse=svmpredict(train_label,train_data,model);figure;%畫圖比較預(yù)測(cè)值和實(shí)際值subplot(2,1,1);plot(train_label,'-o');holdon;plot(ptrain,'r-s');gridon;legend('original','predict');title('TrainSetRegressionPredictbySVM');Meansquarederror=14537(regression)Squaredco

9、rrelationcoefficient=(regression)4 .核函數(shù)為sigmoid:tanh(gamma*u'*v+coef0)神經(jīng)元的非線性作用函數(shù)train_label=data(1:50,1);train_data=data(1:50,2:14);bestmse,bestc,bestg=SVMcgForRegress(train_label,train_data);cmd='-c',num2str(bestc),'-g',num2str(bestg),'-s3-t3-p'model=svmtrain(train_labe

10、l,train_data,cmd);ptrain,mse=svmpredict(train_label,train_data,model);figure;%畫圖比較預(yù)測(cè)值和實(shí)際值subplot(2,1,1);plot(train_label,'-o');holdon;plot(ptrain,'r-s');gridon;legend('original','predict');title('TrainSetRegressionPredictbySVM');注意：第一部分在建模時(shí)僅采用的是前50組數(shù)據(jù)生成的測(cè)試效率-因

11、素模型，當(dāng)選取的訓(xùn)練集越多（接近100）時(shí)，他的效果是越差的，舉例說明如下：核函數(shù)為 RBFMean squared error = (regression)Squared correlation coefficient = (regression)下圖為江良學(xué)長(zhǎng)的測(cè)試成本-因素結(jié)果Mean squared error = (regression)Squared correlation coefficient = （regression）選取的樣本越多，得到的MS喇大（雖然mse增加，但對(duì)樣本的預(yù)測(cè)效果肯定會(huì)更好，因?yàn)殡S著樣本數(shù)增加，學(xué)習(xí)能力肯定提高），而相關(guān)系數(shù)反而有所提高（接近于1最佳）；

12、問題提出：為什么 bestmse =+004與實(shí)際訓(xùn)練出來的 Mean squared error = （regression） 相距甚選-變神錘網(wǎng)絡(luò)的摘T-昵神蛭網(wǎng)絡(luò)實(shí)際材據(jù)r多元雄壯回歸加何r-密而續(xù)住回心實(shí)際收搪Part2:從參數(shù)優(yōu)化方法選取不同上比較那種參數(shù)選取更優(yōu)此比較基于RBF核函數(shù)而言1.基于網(wǎng)格尋優(yōu)方法FnrlPuinpJSIfitRflJI 撒把：評(píng)本笫 線建,潮流?；虺杀綯iflir 95 R自/白匕&自。上 1 by 口丫獨(dú)白一口13 ! -61- prrdKf(ran hjagrcsiiETiBy M品 1r代碼：train_label=data(1:50,1)

13、;train_data=data(1:50,2:14);bestmse,bestc,bestg=SVMcgForRegress(train_label,train_data)%優(yōu)化方法暫定為網(wǎng)格尋優(yōu)cmd='-c',num2str(bestc),'-g',num2str(bestg),'-s3-t2-p'model=svmtrain(train_label,train_data,cmd);ptrain,mse,dec_value=svmpredict(train_label,train_data,model);結(jié)果：bestmse=+004bes

14、tc=bestg=Meansquarederror=(regression)Squaredcorrelationcoefficient=(regression)1t卻匕k砧串.仁'LKffili.-J.-.Thrvtr.d目MtC=：'=576?=口口口+4口63CVfflW=15541尊犯工2 .基于遺傳算法尋優(yōu)train_label=data(1:50,1);train_data=data(1:50,2:14);bestCVmse,bestc,bestg,ga_option=gaSVMcgForRegress(train_label,train_data)cmd='

15、-c',num2str(bestc),'-g',num2str(bestg),'-s3-t2-p'model=svmtrain(train_label,train_data,cmd);ptrain,mse,dec_value=svmpredict(train_label,train_data,model);結(jié)果：bestCVmse=+004bestc=bestg=ga_option=maxgen:200sizepop:20ggap:cbound:0100gbound:01000v:5Meansquarederror=(regression)Squared

16、correlationcoefficient=(regression)口或初六小江：好般打必(?rib靖=1沖和珞決Sp姓初3 .基于pso尋優(yōu)(在這里使用啟發(fā)式算法PSO來進(jìn)行參數(shù)尋優(yōu)，用網(wǎng)格劃分(gridsearch)來尋找最佳的參數(shù)c和g,雖然采用網(wǎng)格搜索能夠找到在CV意義下的最高的分類準(zhǔn)確率，即全局最優(yōu)解，但有時(shí)候如果想在更大的范圍內(nèi)尋找最佳的參數(shù)c和g會(huì)很費(fèi)時(shí)，采用啟發(fā)式算法就可以不必遍歷網(wǎng)格內(nèi)的所有的參數(shù)點(diǎn)，也能找到全局最優(yōu)解)代碼：train_label=data(1:50,1);train_data=data(1:50,2:14);bestCVmse,bestc,bestg,p

17、so_option=psoSVMcgForRegress(train_label,train_data)cmd='-c',num2str(bestc),'-g',num2str(bestg),'-s3-t2-p'model=svmtrain(train_label,train_data,cmd);ptrain,mse,dec_value=svmpredict(train_label,train_data,model);結(jié)果：+004bestc=bestg=pso_option=c1:c2:maxgen:200sizepop:20k:wV:1wP:

18、1v:5popcmax:100popcmin:popgmax:1000popgmin:Meansquarederror=(regression)Squaredcorrelationcoefficient=(regression)注意：仍是以前50組樣本為例Part3:主元分析仍然以RBF為核函數(shù)，進(jìn)行主元分析之后參數(shù)優(yōu)化，最終得到MSE代碼：train_label=data(1:50,1);train_data=data(1:50,2:14);test_label=data(51:100,1);test_data=data(51:100,2:14);train_pca,test_pca=pcaForSVM(train_data,test_