分类预测 | MATLAB实现DBN-SVM深度置信网络结合支持向量机多输入分类预测
目录
预测效果
基本介绍
1.分类预测 | MATLAB实现DBN-SVM深度置信网络结合支持向量机多输入分类预测
2.代码说明：要求于Matlab 2021版及以上版本。
程序设计
- 完整程序和数据获取方式1：同等价值程序兑换；
- 完整程序和数据获取方式2：私信博主回复 MATLAB实现DBN-SVM深度置信网络结合支持向量机多输入分类预测获取。
clike
%% 划分训练集和测试集
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);
%% 数据归一化
[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); % 训练模型
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 训练权重移植，添加输出层
nn = dbnunfoldtonn(dbn, num_class);
%% 反向调整网络
opts.numepochs = 576; % 反向微调次数
opts.batchsize = M; % 每次反向微调样本数 需满足：（M / batchsize = 整数）
nn.activation_function = 'sigm'; % 激活函数
nn.learningRate = 2.9189; % 学习率
nn.momentum = 0.5; % 动量参数
nn.scaling_learningRate = 1; % 学习率的比例因子
[nn, loss, accu] = nntrain(nn, p_train, t_train, opts); % 训练
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%% 仿真预测
T_sim1 = nnpredict(nn, p_train);
T_sim2 = nnpredict(nn, p_test );
%% 性能评价
error1 = sum((T_sim1' == T_train)) / M * 100 ;
error2 = sum((T_sim2' == T_test )) / N * 100 ;
https://blog.csdn.net/kjm13182345320/article/details/131174983
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原文链接：https://blog.csdn.net/kjm13182345320/article/details/130462492参考资料
[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