BP人工神经网络-反向传播法-matlab-数据+代码

% BP人工神经网络算法 % http://www.cnblogs.com/qiusuo/p/4067285.html clc clear %% 加载数据 load data1 c1 load data2 c2 load data3 c3 load data4 c4 %四个特征信号矩阵合成一个矩阵 data(1:500,:)=c1(1:500,:); data(501:1000,:)=c2(1:500,:); data(1001:1500,:)=c3(1:500,:); data(1501:2000,:)=c4(1:500,:); %随机选取数据进行训练和检验 rand =rands(1,2000); [rand,index]=sort(rand); train_data=data( index(1:1500),:); test_data=data( index(1501:2000),:); input_train=train_data(:,2:25); output_train=zeros(2000,4); for i=1:1500 switch train_data(i,1) case 1 output_train(i,1)=1; case 2 output_train(i,2)=1; case 3 output_train(i,3)=1; case 4 output_train(i,4)=1; end end [input_train,setting]=mapminmax(input_train'); input_train=input_train'; input_test=test_data(:,2:25); output_test=test_data(:,1); input_test=mapminmax('apply',input_test',setting); input_test=input_test'; %% 构建人工神经网络 in_num=24;%输入的因子数 mid_num=25;%中间层的个数 out_num=4;%输出的因子数 W1=rands(mid_num,in_num);%每个输入对中间层的权重 TD1=rands(1,mid_num);%中间层的阈值 W2=rands(out_num,mid_num);%中间层对输出的权重 TD2=rands(1,out_num);%输出的阈值 %学习率 xite=0.1;%权重的学习率 alfa=0.01;%阈值的学习率 loop_num=100;%训练迭代的次数 %% 网络训练 for loop=1:loop_num for index=1:1500 input=input_train(index,:); % 计算中间层输出 mid_out=input*W1'+TD1; mid_out=1./(1+exp(-mid_out)); % 计算网络的输出 output=mid_out*W2'+TD2; output=1./(1+exp(-output)); % 计算权重的下降梯度 E= output_train(index,:) - output; dW2 = E .* output .* (1 - output) * xite; dW1 = dW2 * W2 .* mid_out .* (1-mid_out) * xite; % 更新权重 W2=W2 + diag( dW2) * repmat(mid_out,out_num,1); W1=W1 + diag(dW1) * repmat(input,mid_num,1); % 更新阈值 TD2=TD2+dW2*xite; TD1=TD1+dW1*xite; end end %% 数据验证 valid_output=zeros(500,4); for index=1:500 input=input_test(index,:); mid_out=input*W1'+TD1; mid_out=1./(1+exp(-mid_out)); output=mid_out*W2'+TD2; output=1./(1+exp(-output)); valid_output(index,:)=output; end [max_value,max_index]=max(valid_output'); valid_output=max_index'; %% 计算错误率 count=[0 0 0 0]; e_count=[0 0 0 0]; for i=1:500 switch output_test(i) case 1 count(1)=count(1)+1; if valid_output(i)~=1 e_count(1)=e_count(1)+1; end case 2 count(2)=count(2)+1; if valid_output(i)~=2 e_count(2)=e_count(2)+1; end case 3 count(3)=count(3)+1; if valid_output(i)~=3 e_count(3)=e_count(3)+1; end case 4 count(4)=count(4)+1; if valid_output(i)~=4 e_count(4)=e_count(4)+1; end end end 1-e_count./count