分類預(yù)測 | MATLAB實現(xiàn)DBN-SVM深度置信網(wǎng)絡(luò)結(jié)合支持向量機多輸入分類預(yù)測

預(yù)測效果

基本介紹

1.分類預(yù)測 | MATLAB實現(xiàn)DBN-SVM深度置信網(wǎng)絡(luò)結(jié)合支持向量機多輸入分類預(yù)測
2.代碼說明：要求于Matlab 2021版及以上版本。

程序設(shè)計

• 完整程序和數(shù)據(jù)獲取方式1：同等價值程序兌換；
• 完整程序和數(shù)據(jù)獲取方式2：私信博主回復(fù) MATLAB實現(xiàn)DBN-SVM深度置信網(wǎng)絡(luò)結(jié)合支持向量機多輸入分類預(yù)測獲取。

%%  劃分訓(xùn)練集和測試集
P_train = res(1: num_train_s, 1: f_)';
T_train = res(1: num_train_s, f_ + 1: end)';
M = size(P_train, 2);

P_test = res(num_train_s + 1: end, 1: f_)';
T_test = res(num_train_s + 1: end, f_ + 1: end)';
N = size(P_test, 2);

%%  數(shù)據(jù)歸一化
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input);

[t_train, ps_output] = mapminmax(T_train, 0, 1);
t_test = mapminmax('apply', T_test, ps_output);
dbn = dbnsetup(dbn, p_train, opts);     % 建立模型
dbn = dbntrain(dbn, p_train, opts);     % 訓(xùn)練模型
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  訓(xùn)練權(quán)重移植，添加輸出層
nn = dbnunfoldtonn(dbn, num_class);

%%  反向調(diào)整網(wǎng)絡(luò)
opts.numepochs          = 576;  % 反向微調(diào)次數(shù)
opts.batchsize          = M;            % 每次反向微調(diào)樣本數(shù) 需滿足：（M / batchsize = 整數(shù)）

nn.activation_function  = 'sigm';       % 激活函數(shù)
nn.learningRate         = 2.9189;       % 學(xué)習(xí)率
nn.momentum             = 0.5;          % 動量參數(shù)
nn.scaling_learningRate = 1;            % 學(xué)習(xí)率的比例因子

[nn, loss, accu] = nntrain(nn, p_train, t_train, opts);  % 訓(xùn)練
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  仿真預(yù)測 
T_sim1 = nnpredict(nn, p_train); 
T_sim2 = nnpredict(nn, p_test );

%%  性能評價
error1 = sum((T_sim1' == T_train)) / M * 100 ;
error2 = sum((T_sim2' == T_test )) / N * 100 ;
https://blog.csdn.net/kjm13182345320/article/details/131174983
版權(quán)聲明：本文為CSDN博主「機器學(xué)習(xí)之心」的原創(chuàng)文章，遵循CC 4.0 BY-SA版權(quán)協(xié)議，轉(zhuǎn)載請附上原文出處鏈接及本聲明。
原文鏈接：https://blog.csdn.net/kjm13182345320/article/details/130462492

參考資料

[1] https://blog.csdn.net/kjm13182345320/article/details/129679476?spm=1001.2014.3001.5501
[2] https://blog.csdn.net/kjm13182345320/article/details/129659229?spm=1001.2014.3001.5501
[3] https://blog.csdn.net/kjm13182345320/article/details/129653829?spm=1001.2014.3001.5501文章來源地址http://www.zghlxwxcb.cn/news/detail-657600.html

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