MATLAB人工神经网络Word格式.docx
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MATLAB人工神经网络Word格式.docx
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errorfunction
Step1:
initializetheweightparametersandotherparameters
defaultpoints=50;
%%隐含层节点数
inputpoints=2;
%%输入层节点数
outputpoints=2;
%%输出层节点数
Testerror=zeros(1,100);
%每个测试点的误差记录
a=zeros(1,inputpoints);
%输入层节点值
y=zeros(1,outputpoints);
%样本节点输出值
w=zeros(inputpoints,defaultpoints);
%输入层和隐含层权值
%初始化权重很重要,比如用rand函数初始化则效果非常不确定,不如用zeros初始化
v=zeros(defaultpoints,outputpoints);
%隐含层和输出层权值
bin=rand(1,defaultpoints);
%隐含层输入
bout=rand(1,defaultpoints);
%隐含层输出
base1=0*ones(1,defaultpoints);
%隐含层阈值,初始化为0
cin=rand(1,outputpoints);
%输出层输入
cout=rand(1,outputpoints);
%输出层输出
base2=0*rand(1,outputpoints);
%%输出层阈值
error=zeros(1,outputpoints);
%拟合误差
errors=0;
error_sum=0;
%误差累加和
error_rate_cin=rand(defaultpoints,outputpoints);
%误差对输出层节点权值的导数
error_rate_bin=rand(inputpoints,defaultpoints);
%误差对输入层节点权值的导数
alfa=1;
%%%%alfa是隐含层和输出层权值-误差变化率的系数,影响很大
belt=0.5;
%%%%belt是隐含层和输入层权值-误差变化率的系数,影响较小
gama=3;
%%%%gama是误差放大倍数,可以影响跟随速度和拟合精度
trainingROUND=5;
%训练次数,有时训练几十次比训练几百次上千次效果要好
sampleNUM=100;
%样本点数
x1=zeros(sampleNUM,inputpoints);
%样本输入矩阵
y1=zeros(sampleNUM,outputpoints);
%样本输出矩阵
x2=zeros(sampleNUM,inputpoints);
%测试输入矩阵
y2=zeros(sampleNUM,outputpoints);
%测试输出矩阵
observeOUT=zeros(sampleNUM,outputpoints);
%%拟合输出监测点矩阵
i=0;
j=0;
k=0;
%%%%其中j是在一个训练周期中的样本点序号,不可引用
h=0;
o=0;
%%%%输入层序号,隐含层序号,输出层序号
x=0:
0.1:
50;
%%%%步长
Step2:
selectsampleinputandoutput
forj=1:
sampleNUM%这里给样本输入和输出赋值,应根据具体应用来设定
x1(j,1)=x(j);
x2(j,1)=0.3245*x(2*j)*x(j);
temp=rand(1,1);
x1(j,2)=x(j);
x2(j,2)=0.3*x(j);
y1(j,1)=sin(x1(j,1));
y1(j,2)=cos(x1(j,2))*cos(x1(j,2));
y2(j,1)=sin(x2(j,1));
y2(j,2)=cos(x2(j,2))*cos(x2(j,2));
end
foro=1:
outputpoints
y1(:
o)=(y1(:
o)-min(y1(:
o)))/(max(y1(:
o))-min(y1(:
o)));
%归一化,使得输出范围落到[0,1]区间上,当激活函数为对数S型时适用
y2(:
o)=(y2(:
o)-min(y2(:
o)))/(max(y2(:
o))-min(y2(:
fori=1:
inputpoints
x1(:
i)=(x1(:
i)-min(x1(:
i)))/(max(x1(:
i))-min(x1(:
i)));
%输入数据归一化范围要和输出数据的范围相同,[0,1]
x2(:
i)=(x2(:
i)-min(x2(:
i)))/(max(x2(:
i))-min(x2(:
fori=1:
inputpoints%%%%%样本输入层赋值
a(i)=x1(j,i);
end
foro=1:
outputpoints%%%%%样本输出层赋值
y(o)=y1(j,o);
Step3:
computetheinputandoutputoftheneuralnetworkhiddenlayer
forh=1:
defaultpoints
bin(h)=0;
bin(h)=bin(h)+a(i)*w(i,h);
bin(h)=bin(h)-base1(h);
bout(h)=1/(1+exp(-bin(h)));
%%%%%%隐含层激励函数为对数激励
Step4:
computetheinputandoutputoftheneuralnetworkoutputlayer,computetheerrorfunction’spartialderivativesforeachneuronoftheoutputlayer.
temp_error=0;
cin(o)=0;
forh=1:
cin(o)=cin(o)+bout(h)*v(h,o);
cin(o)=cin(o)-base2(o);
cout(o)=1/(1+exp(-cin(o)));
%%%%%%输出层激励函数为对数激励
observeOUT(j,o)=cout(o);
error(o)=y(o)-cout(o);
temp_error=temp_error+error(o)*error(o);
%%%%%记录实际误差,不应该乘伽玛系数
error(o)=gama*error(o);
Testerror(j)=temp_error;
error_sum=error_sum+Testerror(j);
error_rate_cin(o)=error(o)*cout(o)*(1-cout(o));
Step5:
computetheerrorfunction’spartialderivativesforeachneuronofthehiddenlayer,usingtheerrorsandweights
defaultpoints
error_rate_bin(h)=0;
error_rate_bin(h)=error_rate_bin(h)+error_rate_cin(o)*v(h,o);
error_rate_bin(h)=error_rate_bin(h)*bout(h)*(1-bout(h));
Step6:
modifythelinkweightsofhiddenlayerandoutputlayer,andhiddenlayerandinputlayer.
base1(h)=base1(h)-5*error_rate_bin(h)*bin(h);
v(h,o)=v(h,o)+alfa*error_rate_cin(o)*bout(h);
%
%base1(i)=base1(i)+0.01*alfa*error(i);
w(i,h)=w(i,h)+belt*error_rate_bin(h)*a(i);
%base2=base2+0.01*belt*out_error;
Step7:
computetheoverallerrorsum.
temp_error=temp_error+error(o)*error(o);
error_sum=error_sum+Testerror(j);
Step8:
judgewhethertheerrorratesatisfytheprecision,ifitdoesthenreturn,elsegobacktostep3untilitexceedthelimittime.
parameterdesigning
1.50hiddennodes,whenalfa*gama=3,theerrorsumisleast,beltonlyinfluencealittle.
2.100hiddennodes,whenalfa*gama=1.5,theerrorsumisleast,beltonlyinfluencealittle.
Andtheminimumerrorsumisapproximatelythesameas50hiddennodes.
3.200hiddennodes,whenalfa*gama=0.7,theerrorsumisleast,beltonlyinfluencealittle.
4.base1influencetheminimumerrorsumverylittle,butitdoeshelpstabilizethesystem.
4.Performanceanalysis
A50hiddenpoints,trainingfor200times,testthenetworkwithtestingsamples,theshapeisbasicallyexpected,andtheerrorsumis1.49.
10hiddenpoints,trainingfor200times,testthenetworkwithtestingsamples,theshapeisbasicallyexpected,andtheerrorsumis0.89416.
20hiddenpoints,trainingfor200times,testthenetworkwithtestingsamples,theshapeisbasicallyexpected,andtheerrorsumis0.89416.
ProblemB:
20hiddenpoints,trainingfor200times,testthenetworkwithtestingsamples,theshapeisbasicallyexpected,andtheerrorsumis2.3833.
alfa=0.5;
belt=0.5;
gama=3;
learningrate:
alfa*gama=1.5
Fromthefirstandsecondfigure,wecometotheconclusion:
ANNwithmorehiddenpointsperformancesbetter;
Fromfigure2~4,wecometotheconclusion:
thisANNperformancebestatthelearningrate1.5.
ProblemC:
Fromthetwofigure,wecanseethatwithalltheotherparametersthesame,ANNwith50hiddennodesonlyhasanerrorsumof0.89102,butANNwith20hiddennodeshasanerrorsumof1.8654.Thus,wecancometoaconlusionthatANNwithmorehiddennodesperformancesbetter.
Fromthetwofigures,ANNwithtrainingtimeof10producesthesameerrorsum(0.89)astrainingtimeof100.
5.Conclusion
ArtificialneuralnetworksareacompletelydifferentapproachtoAIinprocessinginformation.Insteadofbeingprogrammedexactlywhatdotostepbystep,theyaretrainedwithsampledata.Theycanthenusethispreviousdatatoclassifynewthingsinwaysthatwouldbedifficultforhumanstodo.Theycanbeverypowerfulclassificationtoolssincetheyarecapableofhandlingmassiveamountsofdatathroughparallelprocessing.Learningisaccomplishedsupervisedandunsupervisedmodels.Thealgorithmicadvancestoneuralnetworkshaveledtoadvancementsinmanyfieldslikemodelsimulationandpatternrecognition.Thus,duetothewidevarietyofhigh-complexityproblemsneuralnetworkscansolvetheyareawidelyresearchedareaandhavemanypromisingcapabilities.
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