BP人工神经网络算法的MATLAB实现

1、%清空环境变量clcclear% 训练数据预测数据提取及归一化%载四类语音信号load datal clload 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);% 俞入输出数据input=data(:,2:25);outputl =dat

2、a(:,1);%输出从1维变成4维for i=1:2000switch outputl(i)case 1output(i,:)=1 0 0 0;case 2output(i,:)=0 1 0 0;case 3output(i,:)=0 0 1 0;case 4output(i,:)=0 0 0 1;endend%$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

3、_test=output(n(1501:2000),:)'喏俞入数据归一化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;

4、%网络训练for ii=1:10E(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:midnumI(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:midnumS=1/(1+exp(-I(j);F

5、I(j)=S*(1-S);endfor k=1:1:innumfor j=1:1:midnumdw1(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 endw1=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=

6、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=1:1for i=1:500%1500% 急含层输出I(j)=inputn_test(:,i)'*w1(j,:)'+b1(j);Iout(j)=1/(1+exp(-I(j);endfore(:,i)=w2'*Iout'+b2;endend%吉果分析% 艮据网络输出找出数据属于哪类for i=1:500out

7、put_fore(i)=find(fore(:,i)=max(fore(:,i); end%BFW络预测误差error=output_fore-output1(n(1501:2000)'%B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)刎出误差图figure(2)plot(error)title('BP网络分类误差,'fontsize',12)

8、xlabel(' 语音信号','fontsize',12)ylabel('分类误差','fontsize',12)%print -dtiff -r600 1-4k=zeros(1,4);%找出判断错误的分类属于哪一类for i=1:500if error(i)=0b,c=max(output_test(:,i);switch ccase 1 k(1)=k(1)+1;case 2 k(2)=k(2)+1;case 3k(3)=k(3)+1;case 4k(4)=k(4)+1;endendend%找出每类的个体和kk=zeros(1,4);for i=1:500b,c=max(output_test