周末d1.txt

小波神经网络对短期电力负荷的预测

P=[0.548684351	0.487772711	0.725878348	0	1	0.694587535	0.221722654	0.591921982	0.584154409	0.587138973	0.478574946	0.601312437	0.0999;
0.44086219	0.504942398	0.615156777	0	1	0.277820817	0.531468928	0.388750125	0.489327479	0.75461315	0.205773533	0.604891976	0.0970]';
T=[0.44086219	0.504942398	0.615156777	0	1	0.277820817	0.531468928	0.388750125	0.489327479	0.75461315	0.205773533	0.604891976;
0.519607163	0.437917667	0.856119623	0	1	0.921144545	0.120347056	0.396175005	0.875968248	0.646735093	0.38477386	0.798471479]';
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=[0.519607163
	0.437917667
	0.856119623
	0
	1
	0.921144545
	0.120347056
	0.396175005
	0.875968248
	0.646735093
	0.38477386
	0.798471479
	0.0978];
Out1=sim(net,P_test1);

P_test2=[0.328331381
	0.732990072
	0.356635013
	0
	1
	0.432986989
	0.34663008
	0.321178393
	0.601057532
	0.644484183
	0.238653882
	0.563282358
	0.0922];
Out2=sim(net,P_test2);