BP神经网络源代码--数据分类(共5页).doc
精选优质文档-倾情为你奉上BP网络源代码：clcclear % 训练数据预测数据提取及归一化 %下载四类语音信号load data1 c1load data2 c2load data3 c3load 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,:); %从1到2000间随机排序k=rand(1,2000);m,n=sort(k); %输入输出数据input=data(:,2:25);output1 =data(:,1); %把输出从1维变成4维for i=1:2000 switch output1(i) case 1 output(i,:)=1 0 0 0; case 2 output(i,:)=0 1 0 0; case 3 output(i,:)=0 0 1 0; case 4 output(i,:)=0 0 0 1; endend %随机提取1500个样本为训练样本，500个样本为预测样本input_train=input(n(1:1500),:)'output_train=output(n(1:1500),:)'input_test=input(n(1501:2000),:)'output_test=output(n(1501:2000),:)' %输入数据归一化inputn,inputps=mapminmax(input_train); % 网络结构初始化innum=24;midnum=25;outnum=4; %权值初始化w1=rands(midnum,innum);b1=rands(midnum,1);w2=rands(midnum,outnum);b2=rands(outnum,1); w2_1=w2;w2_2=w2_1;w1_1=w1;w1_2=w1_1;b1_1=b1;b1_2=b1_1;b2_1=b2;b2_2=b2_1; %学习率xite=0.1alfa=0.01; % 网络训练for ii=1:10 E(ii)=0; for i=1:1:1500 % 网络预测输出 x=inputn(:,i); % 隐含层输出 for j=1:1:midnum I(j)=inputn(:,i)'*w1(j,:)'+b1(j); Iout(j)=1/(1+exp(-I(j); end % 输出层输出 yn=w2'*Iout'+b2; % 权值阀值修正 %计算误差 e=output_train(:,i)-yn; E(ii)=E(ii)+sum(abs(e); %计算权值变化率 dw2=e*Iout; db2=e' for j=1:1:midnum S=1/(1+exp(-I(j); FI(j)=S*(1-S); end for k=1:1:innum for j=1:1:midnum dw1(k,j)=FI(j)*x(k)*(e(1)*w2(j,1)+e(2)*w2(j,2)+e(3)*w2(j,3)+e(4)*w2(j,4); db1(j)=FI(j)*(e(1)*w2(j,1)+e(2)*w2(j,2)+e(3)*w2(j,3)+e(4)*w2(j,4); end end w1=w1_1+xite*dw1' b1=b1_1+xite*db1' w2=w2_1+xite*dw2' b2=b2_1+xite*db2' w1_2=w1_1;w1_1=w1; w2_2=w2_1;w2_1=w2; b1_2=b1_1;b1_1=b1; b2_2=b2_1;b2_1=b2; endend % 语音特征信号分类inputn_test=mapminmax('apply',input_test,inputps); for ii=1:1 for i=1:500%1500 %隐含层输出 for j=1:1:midnum I(j)=inputn_test(:,i)'*w1(j,:)'+b1(j); Iout(j)=1/(1+exp(-I(j); end fore(:,i)=w2'*Iout'+b2; endend % 结果分析%根据网络输出找出数据属于哪类for i=1:500 output_fore(i)=find(fore(:,i)=max(fore(:,i);end %BP网络预测误差error=output_fore-output1(n(1501:2000)' %画出预测语音种类和实际语音种类的分类图figure(1)plot(output_fore,'r')hold onplot(output1(n(1501:2000)','b')legend('预测语音类别','实际语音类别') %画出误差图figure(2)plot(error)title('BP网络分类误差','fontsize',12)xlabel('语音信号','fontsize',12)ylabel('分类误差','fontsize',12) %print -dtiff -r600 1-4 k=zeros(1,4); %找出判断错误的分类属于哪一类for i=1:500 if error(i)=0 b,c=max(output_test(:,i); switch c case 1 k(1)=k(1)+1; case 2 k(2)=k(2)+1; case 3 k(3)=k(3)+1; case 4 k(4)=k(4)+1; end endend %找出每类的个体和kk=zeros(1,4);for i=1:500 b,c=max(output_test(:,i); switch c case 1 kk(1)=kk(1)+1; case 2 kk(2)=kk(2)+1; case 3 kk(3)=kk(3)+1; case 4 kk(4)=kk(4)+1; endend %正确率rightridio=(kk-k)./kk专心-专注-专业
