周末a3.txt

小波神经网络对短期电力负荷的预测

P=[0	0.075747508	0.161461794	0.256478405	0.368770764	0.497342193	0.606312292	0.69269103	0.774086379	0.840199336	0.914950166	1	0.0999;
0	0.123454442	0.257561347	0.40041849	0.580369032	0.791325851	0.927144759	0.980787521	1	0.955297698	0.916682519	0.887388244	0.0970]';
T=[0	0.123454442	0.257561347	0.40041849	0.580369032	0.791325851	0.927144759	0.980787521	1	0.955297698	0.916682519	0.887388244;
0	0.116008571	0.243036425	0.380165289	0.550352005	0.748393021	0.885521886	0.955310683	0.998775635	0.990817264	0.990205081	1]';
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
	0.116008571
	0.243036425
	0.380165289
	0.550352005
	0.748393021
	0.885521886
	0.955310683
	0.998775635
	0.990817264
	0.990205081
	1
	0.0978];
Out1=sim(net,P_test1);

P_test2=[0
	0.122793842
	0.256853173
	0.400863688
	0.581674803
	0.79402929
	0.929966204
	0.98253849
	1
	0.953060458
	0.911753661
	0.879647015
	0.0922];
Out2=sim(net,P_test2);