1-6.txt

小波神经网络对短期电力负荷的预测

P=[0.52122096	0.549166533	0.555793434	0.547731482	0.512248975	0.445205705	0.411688956	0.412768161	0.41275066	0.426653444	0.435339585	0.43543438	0.1004;
0.54127667	0.736265659	0.796496959	0.712348146	0.574644519	0.333767446	0.231095685	0.288249792	0.346741239	0.468084703	0.524633581	0.494832942	0.1009;
0.588480217	0.761933235	0.805130598	0.711196022	0.548164622	0.271113769	0.161443218	0.241275212	0.336166489	0.517717919	0.603625545	0.56800595	0.0983;
0.603450539	0.901267337	1	0.887441847	0.68978693	0.334365384	0.169012236	0.222943313	0.266169844	0.385986379	0.458803103	0.458459945	0.0968]';
T=[0.54127667	0.736265659	0.796496959	0.712348146	0.574644519	0.333767446	0.231095685	0.288249792	0.346741239	0.468084703	0.524633581	0.494832942;
0.588480217	0.761933235	0.805130598	0.711196022	0.548164622	0.271113769	0.161443218	0.241275212	0.336166489	0.517717919	0.603625545	0.56800595;
0.603450539	0.901267337	1	0.887441847	0.68978693	0.334365384	0.169012236	0.222943313	0.266169844	0.385986379	0.458803103	0.458459945;
0.577354198	0.731321734	0.778908836	0.718342108	0.597153233	0.379301142	0.279207805	0.311677289	0.341718561	0.422418294	0.468382214	0.463879742]';
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=0.008;
LP.lr=0.1;
net=train(net,P,T);

P_test=[0.577354198
	0.731321734
	0.778908836
	0.718342108
	0.597153233
	0.379301142
	0.279207805
	0.311677289
	0.341718561
	0.422418294
	0.468382214
	0.463879742
	0.0996];
Out=sim(net,P_test);