matlab神經(jīng)網(wǎng)絡(luò)30個案例分析源代碼demochapter

1、SVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間SVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸盤指數(shù)變化趨勢和變化空間-上證指數(shù)開該案例作者：1：本人長期駐扎在此板塊里，對該案例提問，做到有問必答。本套書籍為：v2：點此從當當預(yù)定本書：神經(jīng)網(wǎng)絡(luò)30個案例分析。方式v/vbuy.html。3：此案例有配套的教學，4：此案例為神經(jīng)網(wǎng)絡(luò)30個案例分析）。案例，請注明出處（5：若此案例碰巧與您的研究有關(guān)聯(lián)，提意見，要求等，考慮后可以加在案例里。by lifaruto farutos Studio E http:/ HYPERLINK http:/v/ http:/vContents清空環(huán)境變量 原

2、始數(shù)據(jù)的提取對原始數(shù)據(jù)進行模糊信息?；?利用SVM對Low進行回歸對于Low的回歸結(jié)果分析 利用SVM對R進行回歸對于R的回歸結(jié)果分析 利用SVM對Up進行回歸對于Up的回歸結(jié)果分析子函數(shù) SVMcgForRegress.m清空環(huán)境變量原始數(shù)據(jù)的提取% 載入測試數(shù)據(jù)上證指數(shù)(1990.12.19-2009.08.19)% 數(shù)據(jù)是一個4579*6的double型的矩陣,每一行表示每一天的上證指數(shù)% 6列分別表示當天上證指數(shù)的開盤指數(shù),指數(shù)最高值,指數(shù)最低值,收盤指數(shù),當日交易量,當日交易額. load chapter15_sh.mat;% 提取數(shù)據(jù)ts = sh_open;time = leng

3、th(ts);% 畫出原始上證指數(shù)的開盤數(shù)figure; plot(ts,LineWid ,2);title(上證指數(shù)的開盤數(shù)(1990.12.20-2009.08.19),FontSize,12);grid on; snapnow;tic;close all; clear; clc;format compact;function chapter15SVM神經(jīng)網(wǎng)絡(luò)的信息粒化時序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間對原始數(shù)據(jù)進行模糊信息?；痺in_num = floor(time/5); tsx = 1:win_num;tsx = tsx; Low,R,Up=FIG_D(ts,triangle

4、,win_num);% 模糊信息粒化可視化圖figure; hold on;plot(Low,b+);plot(R,r*);plot(Up,gx); hold off;legend(Low,R,Up,2);title(模糊信息?；梢暬瘓D,FontSize,12); grid on;snapnow;SVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間利用SVM對Low進行回歸% 數(shù)據(jù)預(yù)處理,將Low進行歸一化處理% mapm ax為自帶的 函數(shù) low,l ps ax(Low); low_ps.ymin = 100;low_ps.ymax = 500;% 對Low進行歸一化low

5、,low_ps = mapmax(Low,low_ps);% 畫出Low歸一化后的圖 figure; plot(low,b+);title(Low歸一化后的圖像,FontSize,12); grid on;% 對low進行轉(zhuǎn)置,以符合libsvm工具箱的數(shù)據(jù)格式要求low = low; snapnow;% 選擇回歸分析中最佳的SVM參數(shù)c&g% 首先進行粗略選擇bestmse,bestc,bestg = SVMcgForRegress(low,tsx,-10,10,-10,10,3,1,1,0.1);% 打印粗略選擇結(jié)果disp(打印粗略選擇結(jié)果);str = sprf( SVM parame

6、ters for Low:Best Cross Validation MSE = %g Best c = %g Best g =%g,bestmbestc,bestg); disp(str);% 根據(jù)粗略選擇的結(jié)果圖再進行精細選擇bestmse,bestc,bestg = SVMcgForRegress(low,tsx,-4,8,-10,10,3,0.5,0.5,0.05);% 打印精細選擇結(jié)果disp(打印精細選擇結(jié)果);str = sprf( SVM parameters for Low:Best Cross Validation MSE = %g Best c = %g Best g =

7、%g,bestmbestc,bestg); disp(str);% 訓練SVMcmd = -c , num2str(bestc), -g , num2str(bestg) , -s 3 -p 0.1; low_m= svmtrain(low, tsx, cmd);%low predict,low mse = svmpredict(low,tsx,low m);SVM神經(jīng)網(wǎng)絡(luò)的信息粒化時序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間打印粗略選擇結(jié)果SVM parameters for Low:Best Cross Validation MSE =打印精細選擇結(jié)果SVM parameters for L

8、ow:Best Cross Validation MSE = Mean squared error = 22.0054 (regres)35.0879 Best c = 256 Best g = 0.0312535.0177 Best c = 256 Best g = 0.0220971)Squared correlation coefficient = 0. Mean squared error = 85135.8 (regres Squared correlation coefficient = -1 predict_low =2.7968e+00366 (regres)D (regres

9、low_predict = mapmax(reverse,low_predict,low_ps); predict_low = svmpict(1,win_num+1,low_m); predict_low = mapmax(reverse,predict_ low_ps); predict_lowSVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間對于Low的回歸結(jié)果分析figure; hold on;plot(Low,b+); plot(low_predict,r*);legend(original low,predict low,2);SVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證

10、指數(shù)盤指數(shù)變化趨勢和變化空間title(original vs predict,FontSize,12); grid on;figure;error = low_predict - Low; plot(error,ro);title(誤差(predicted data-original data),FontSize,12);grid on; snapnow;SVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間利用SVM對R進行回歸% 數(shù)據(jù)預(yù)處理,將R進行歸一化處理% mapmax為自帶的函數(shù) r,r_p = max(R); r_ps.ymin = 100r_ps.ymax = 5

11、00;% 對R進行歸一化r,r_ps = mapmax(R,r_ps);% 畫出R歸一化后的 figure; plot(r,r*);title(r歸一化后的圖像,FontSize,12); grid on;% 對R進行轉(zhuǎn)置,以符合libsvm工具箱的數(shù)據(jù)格式要求r = r; snapnow;% 選擇回歸分析中最佳的SVM參數(shù)c&g% 首先進行粗略選擇bestmse,bestc,bestg = SVMcgForRegress(r,tsx,-10,10,-10,10,3,1,1,0.1);% 打印粗略選擇結(jié)果disp(打印粗略選擇結(jié)果);str = sprf( SVM parameters for

12、 R:Best Cross Validation MSE = %g Best c = %g Best g =%g,bestmbestc,bestg); disp(str);% 根據(jù)粗略選擇的結(jié)果圖再進行精細選擇bestmse,bestc,bestg = SVMcgForRegress(r,tsx,-4,8,-10,10,3,0.5,0.5,0.05);% 打印精細選擇結(jié)果disp(打印精細選擇結(jié)果);str = sprf( SVM parameters for R:Best Cross Validation MSE = %g Best c = %g Best g =%g,bestmbestc,

13、bestg); disp(str);% 訓練SVMcmd = -c , num2str(bestc), -g , num2str(bestg) , -s 3 -p 0.1; r_m= svmtrain(r, tsx, cmd);%r predict,r mse = svmpredict(r,tsx,low m);SVM神經(jīng)網(wǎng)絡(luò)的信息粒化時序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間打印粗略選擇結(jié)果SVM parameters for R:Best Cross Validation MSE = 22.7823打印精細選擇結(jié)果SVM parameters for R:Best Cross Valida

14、tion MSE = 22.7823 Mean squared error = 26.2007 (regres)Best c = 256 Best g = 0.03125Best c = 256 Best g = 0.03125Squared correlation coefficient = 0. Mean squared error = 84653.7 (regres Squared correlation coefficient = -1 predict_r =2.9500e+00398 (regres)D (regres)r_predict = mapmax(reverse,r_pre

15、dict,r_ps); predict_r = svmpict(1,win_num+1,r_m); predict_r = mapmax(reverse,predic r_ps); predict_rSVM神經(jīng)網(wǎng)絡(luò)的信息粒化時序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間對于R的回歸結(jié)果分析figure; hold on;plot(R,b+); plot(r_predict,r*);legend(original r,predict r,2);SVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間title(original vs predict,FontSize,12); grid o

16、n;figure;error = r_predict - R; plot(error,ro);title(誤差(predicted data-original data),FontSize,12);grid on; snapnow;SVM神經(jīng)網(wǎng)絡(luò)的信息粒化時序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間利用SVM對Up進行回歸% 數(shù)據(jù)預(yù)處理,將up進行歸一化處理% mapm ax為自帶的函數(shù) up,up =ax(Up); up_ps.ymin = 100;up_ps.ymax = 500;% 對Up進行歸一化up,up_ps = mapmax(Up,up_ps);% 畫出Up歸一化后的圖 figur

17、e; plot(up,gx);title(Up歸一化后的圖像,FontSize,12); grid on;% 對up進行轉(zhuǎn)置,以符合libsvm工具箱的數(shù)據(jù)格式要求up = up; snapnow;% 選擇回歸分析中最佳的SVM參數(shù)c&g% 首先進行粗略選擇bestmse,bestc,bestg = SVMcgForRegrep,tsx,-10,10,-10,10,3,1,1,0.5);% 打印粗略選擇結(jié)果disp(打印粗略選擇結(jié)果);str = sprf( SVM parameters for Up:Best Cross Validation MSE = %g Best c = %g Bes

18、t g =%g,bestmbestc,bestg); disp(str);% 根據(jù)粗略選擇的結(jié)果圖再進行精細選擇bestmse,bestc,bestg = SVMcgForRegrep,tsx,-4,8,-10,10,3,0.5,0.5,0.2);% 打印精細選擇結(jié)果disp(打印精細選擇結(jié)果);str = sprf( SVM parameters for Up:Best Cross Validation MSE = %g Best c = %g Best g =%g,bestmbestc,bestg); disp(str);% 訓練SVMcmd = -c , num2str(bestc),

19、-g , num2str(bestg) , -s 3 -p 0.1; up_m= svmtrain(up, tsx, cmd);%up predict,up mse = svmpredict(up,tsx,up m);SVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間打印粗略選擇結(jié)果SVM parameters for Up:Best Cross Validation MSE = 23.8758Best c = 512 Best g = 0.0625打印精細選擇結(jié)果SVM parameters for Up:Best Cross Validation MSE = 23.895

20、Best c = 256 Best g = 0.0220971 Mean squared error = 11.108 (regres)Squared correlation coefficient = 0 Mean squared error = 96798.9 (regres Squared correlation coefficient = -1 predict_up =3.2673e+003625 (regres)D (regres)up_predict = mapmax(reverse,up_predict,up_ps); predict_up = svmpict(1,win_num

21、+1,up_m); predict_up = mapmax(reverse,predict up_ps); predict_upSVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間對于Up的回歸結(jié)果分析figure; hold on;plot(Up,b+); plot(up_predict,r*);legend(original up,predict up,2);SVM神經(jīng)網(wǎng)絡(luò)的信息粒化時序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間Elapsed time is 2725.383778 seconds.title(original vs predict,FontSize,12); g

22、rid on;figure;error = up_predict - Up; plot(error,ro);title(誤差(predicted data-original data),FontSize,12);grid on; toc; snapnow;% web http/forum-31-1.htmlweb http:/orum-31-1.html -new;SVM神經(jīng)網(wǎng)絡(luò)的信息粒化時序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間子函數(shù) SVMcgForRegress.mfunction mse,bestc,bestg = SVMcgForRegress(train_label,train,c

23、min,cmax,gmin,gmax,v,cstep,gstep,msestep)% SVMcgForClass% 輸入:% train_label:訓練集.要求與libsvm工具箱中要求一致.% train:訓練集.要求與libsvm工具箱中要求一致.% cmin:懲罰參數(shù)c的變化范圍的最小值(取以2為底的對數(shù)后),即 c_min = 2(cmin).默認為 -5% cmax:懲罰參數(shù)c的變化范圍的最大值(取以2為底的對數(shù)后),即 c_max = 2(cmax).默認為 5% gmin:參數(shù)g的變化范圍的最小值(取以2為底的對數(shù)后),即 g_min = 2(gmin).默認為 -5% gma

24、x:參數(shù)g的變化范圍的最小值(取以2為底的對數(shù)后),即 g_min = 2(gmax).默認為 5% v:cross validation的參數(shù),即給測試集分為幾部分進行cross validation.默認為 3% cstep:參數(shù)c步進的大小.默認為 1% gstep:參數(shù)g步進的大小.默認為 1% msestep:最后顯示MSE圖時的步進大小.默認為 20% 輸出:% bestacc:Cross Validation 過程中的最高分類準確率% bestc:最佳的參數(shù)c% bestg:最佳的參數(shù)g% about the parameters of SVMcgForRegress if na

25、rgin 10msestep = 0.1;endif nargin 7msestep = 0.1;v = 3;cstep = 1;gstep = 1;endif nargin 6msestep = 0.1;v = 3;cstep = 1;gstep = 1;gmax = 5;endif nargin 5msestep = 0.1;v = 3;cstep = 1;gstep = 1;SVM神經(jīng)網(wǎng)絡(luò)的信息?；瘯r序回歸-上證指數(shù)盤指數(shù)變化趨勢和變化空間神經(jīng)網(wǎng)絡(luò)30個案例分析相關(guān)：神經(jīng)網(wǎng)絡(luò)30個案例分析：v技術(shù)：函數(shù)百科：中文：Published with 7.11gmax = 5;gmin = -5;en