周末d2.txt

小波神经网络对短期电力负荷的预测

P=[1	0.606247228	0.34598492	0	0.044744345	0.596135082	0.653386555	0.303263004	0.383939682	0.75555978	0.653411899	0.158322245	0.0999;
1	0.635656034	0.37400309	0	0.029217922	0.569016095	0.651467741	0.373021337	0.406968103	0.610828623	0.542946825	0.270079987	0.0970]';
T=[1	0.635656034	0.37400309	0	0.029217922	0.569016095	0.651467741	0.373021337	0.406968103	0.610828623	0.542946825	0.270079987;
1	0.635559909	0.372651184	0	0.088843974	0.751906457	0.760325865	0.202536982	0.257566927	0.757399531	0.708160254	0.246779173]';
threshold=[0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1];
net=newff(threshold,[27,12],{'tansig','logsig'},'trainlm');
net.trainParam.epochs=1000;
net.trainParam.goal=0.01;
LP.lr=0.1;
net=train(net,P,T);

P_test1=[1
	0.635559909
	0.372651184
	0
	0.088843974
	0.751906457
	0.760325865
	0.202536982
	0.257566927
	0.757399531
	0.708160254
	0.246779173
	0.0978];
Out1=sim(net,P_test1);

P_test2=[1
	0.793862946
	0.521742209
	0
	0.028158121
	0.673964903
	0.761794277
	0.425267148
	0.465407449
	0.713952703
	0.637865865
	0.317353463
	0.0922];
Out2=sim(net,P_test2);