19-24.txt

小波神经网络对短期电力负荷的预测

P=[0.162104601	0.200827706	0.361865002	0.721871294	0.831554491	0.682366466	0.494661052	0.163629323	0.044023331	0.173987754	0.398886711	0.81933251	0.1004;
0	0.11658075	0.366724751	0.871052058	0.973450471	0.639497568	0.425887848	0.191630969	0.038626298	0.051646942	0.325881556	0.941600716	0.1009;
0.2177013	0.096154312	0.290982357	0.831385077	0.931702123	0.619659237	0.430474358	0.21583775	0.064570779	0.052736029	0.302393572	0.887630388	0.0983;
0.042668022	0.06471599	0.330886517	0.946416903	1	0.475916649	0.309341949	0.336569133	0.204477359	0.045160822	0.270028316	0.890098986	0.0968]';
T=[0	0.11658075	0.366724751	0.871052058	0.973450471	0.639497568	0.425887848	0.191630969	0.038626298	0.051646942	0.325881556	0.941600716;
0.2177013	0.096154312	0.290982357	0.831385077	0.931702123	0.619659237	0.430474358	0.21583775	0.064570779	0.052736029	0.302393572	0.887630388;
0.042668022	0.06471599	0.330886517	0.946416903	1	0.475916649	0.309341949	0.336569133	0.204477359	0.045160822	0.270028316	0.890098986;
0.314429197	0.359805416	0.455338222	0.619431738	0.5895254	0.344630799	0.350267431	0.569515235	0.459869794	0.074154747	0.179820422	0.731963503]';
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_test=[0.314429197
	0.359805416
	0.455338222
	0.619431738
	0.5895254
	0.344630799
	0.350267431
	0.569515235
	0.459869794
	0.074154747
	0.179820422
	0.731963503
	0.0996];
Out=sim(net,P_test);