应用了归一化的预测

应用了归一化的预测，在归一化的过程中使用了premnmx和postmnmx，并在最后给出了这两个函数的应用情况。

 

 

%应用了归一化的预测 clc; clear; originalData=rands(20,14); inputSampledata=originalData'; outputData=rands(20,1); outputSampledata=outputData'; % creating neural network and setting trainging parameters gwwnet=newff(minmax(inputSampledata),[4,1],{'tansig','purelin'},'traingdm'); gwwnet.trainParam.show = 50; gwwnet.trainParam.lr = 0.05; gwwnet.trainParam.epochs = 5000; gwwnet.trainParam.goal = 1e-3; [input,mininput,maxinput,output,minoutput,maxoutput] = premnmx(inputSampledata,outputSampledata); % Preprocess data so that minimum is -1 and maximum is 1 % % Examples % % Here is the code to normalize a given data set so that the inputs % and targets will fall in the range [-1,1]. % p = [-10 -7.5 -5 -2.5 0 2.5 5 7.5 10]; % t = [0 7.07 -10 -7.07 0 7.07 10 7.07 0]; % [pn,minp,maxp,tn,mint,maxt] = premnmx(p,t); % % If you just want to normalize the input, [pn,minp,maxp] = premnmx(p); % % Algorithm % pn = 2*(p-minp)/(maxp-minp) - 1; [gwwnet,tr]=train(gwwnet,input,output); y=sim(gwwnet,input); %data offset (converting the network output data to it original unit) nnoutput = postmnmx(y,minoutput,maxoutput); % % Postprocess data that has been preprocessed by premnmx % Syntax % % [P,T] = postmnmx(PN,minp,maxp,TN,mint,maxt) % [p] = postmnmx(PN,minp,maxp) % Description % % postmnmx postprocesses the network training set % that was preprocessed by premnmx. It converts the data % back into unnormalized units. % postmnmx takes these inputs, % PN -- R x Q matrix of normalized input vectors % minp -- R x 1 vector containing minimums for each P % maxp -- R x 1 vector containing maximums for each P % TN -- S x Q matrix of normalized target vectors % mint -- S x 1 vector containing minimums for each T % maxt -- S x 1 vector containing maximums for each T % and returns, % P -- R x Q matrix of input (column) vectors % T -- R x Q matrix of target vectors % Examples % % In this example we normalize a set of training data with premnmx, % create and train a network using the normalized data, simulate the network, % unnormalize the output of the network using postmnmx, % and perform a linear regression between % the network outputs (unnormalized) and the targets % to check the quality of the network training. % p = [-0.92 0.73 -0.47 0.74 0.29; -0.08 0.86 -0.67 -0.52 0.93]; % t = [-0.08 3.4 -0.82 0.69 3.1]; % [pn,minp,maxp,tn,mint,maxt] = premnmx(p,t); % net = newff(minmax(pn),[5 1],{'tansig' 'purelin'},'trainlm'); % net = train(net,pn,tn); % an = sim(net,pn); % [a] = postmnmx(an,mint,maxt); % [m,b,r] = postreg(a,t); % % Algorithm % p = 0.5(pn+1)*(maxp-minp) + minp; %plot time=1:1:20; plot(time,outputSampledata,'-v',time,nnoutput,'o'); legend('actual output','NN output'); xlabel('time');ylabel('Learning fitting curve'); %scenario1 forecasting process column=10; for i=1:column; SceInput(1,i)=inputSampledata(1,20)*(1.0464^i); SceInput(2,i)=inputSampledata(2,20)*(1.0631^i); SceInput(3,i)=inputSampledata(3,20)*(1.0872^i); SceInput(4,i)=inputSampledata(4,20)*(1.2044^i); SceInput(5,i)=inputSampledata(5,20)*(1.2326^i); SceInput(6,i)=inputSampledata(6,20)*(1.0605^i); SceInput(7,i)=2*(1.01^i); SceInput(8,i)=42*(1.02^i); SceInput(9,i)=inputSampledata(9,20)*(1.1426^i); SceInput(10,i)=inputSampledata(10,20)*(1.017^i); SceInput(11,i)=inputSampledata(11,20)*(1.0205^i); SceInput(12,i)=inputSampledata(12,20)*(1.1336^i); SceInput(13,i)=inputSampledata(13,20)*(1.1599^i); SceInput(14,i)=inputSampledata(14,20)*(1.1783^i); end for j=1:20; for i=1:14; recalldata(i,j)=inputSampledata(i,j); end end for j=21:30; for i=1:14; recalldata(i,j)=SceInput(i,j-20) end end [alterinput,mininput,maxinput] = premnmx(recalldata); %training fvalue=sim(gwwnet,alterinput); %data offset (converting the network output data to it original unit) forecastvalue = postmnmx(fvalue,minoutput,maxoutput); %plot % waitforbuttonpress; % % Wait for key or mouse button press % Syntax % k = waitforbuttonpress % % Description % % k = waitforbuttonpress blocks the caller's execution stream % until the function detects that the user has pressed a mouse button % or a key while the figure window is active. The function returns 0 % if it detects a mouse button press 1 if it detects a key press % % Example % % These statements display text in the Command Window % when the user either clicks a mouse button or % types a key in the figure window: % w = waitforbuttonpress; % if w == 0 % disp('Button press') % else % disp('Key press') % end % clf; figure; time1=1:1:30; time2=1:1:20; plot(time1,forecastvalue,'r-',time2,outputSampledata,'-'); legend('预测曲线','实际曲线'); title('曲线'); xlabel('时间');ylabel('量'); %%%%%%%premnmx及postmnmx使用说明 % clc; % clear; % p = [1 2 3 4; % 5 6 8 9 ]; % [p1 pmin pmax]=premnmx(p); % p2=postmnmx(p1,pmin,pmax); % p1 % pmin % pmax % p2 % % p1 = % % -1.0000 -0.3333 0.3333 1.0000 % -1.0000 -0.5000 0.5000 1.0000 % % % pmin = % % 1 % 5 % % % pmax = % % 4 % 9 % % % p2 = % % 1 2 3 4 % 5 6 8 9