基于BP神经网络的用电量预测-源码

%BP神经网络方法在用电量预测中的应用 clear all; %训练样本存于二维数组中,x[9][4] %x = [15002043,20346236,27629276,22575072; % 20295598,21921608,31412233,23609863; % 21065081,24240841,29866274,23328736; % 21716443,24706176,29987753,23379667; % 21129872,23622805,28921753,22793806; %25560986,29038399,37734355,28395655; % 25073397,28159125,37558426,28395655; % 25254364,20792240,40910291,28945775; % 27795417,31318638,44438585,29317211]; p = [15002043,20346236,27629276; 20295598,21921608,31412233; 21065081,24240841,29866274; 21716443,24706176,29987753; 21129872,23622805,28921753; 25560986,29038399,37734355; 25073397,28159125,37558426; 25254364,20792240,40910291; 27795417,31318638,44438585]' t = [22575072,23609863,23328736,23379667,22793806,28395655,28395655,28945775,29317211]; year = 1995:1:2003; %预测的根据（前3个季度的数据）存于数组data[3]中 data = [21715443,24703176,29987753]'; %由x层到隐层y的权重记录在二维数组wx2y[3][6]中，阈值记录在一维数组cy[6]中； %由y层到v层的权重记录在数组wy2v[6]中，阈值为cv wx2y = zeros(3,6); cy = zeros(6); wy2v = zeros(6); cv = 0; %数据归一化,(0,1) ma1 = max(max(p)); ma2 = max(t); ma = max(ma1,ma2); mi1 = min(min(p)); mi2 = min(t); mi = min(mi1,mi2); for i=1:3 for j = 1:9 p(i,j) = (p(i,j)-mi)/(ma-mi); end end for i = 1:9 t(i) = (t(i)-mi)/(ma-mi); end % NEWFF Create a feed-forward backpropagation network. net = newff([0 1;0 1;0 1],[6 1],{'logsig' 'purelin'},'trainlm'); %画出训练前的拟合图形 Y1 = sim(net,p) %plot(p,t,p,Y1,'--'); plot(year,t,year,Y1); axis([1995,2003,0,1]); title('训练前'); xlabel('年份'); %xlabel('前3个季度的用电量'); ylabel('第四季度的用电量'); %训练精度 net.trainparam.goal = 0.0001; net.trainparam.epochs = 10000;%训练次数 %训练神经网络 [net,tr] = train(net,p,t); %net = train(net,p,t); %输出训练后的权值和阈值 Y = sim(net,p) %plot(p,t,p,Y,'--'); plot(year,t,year,Y,'--'); axis([1995,2003,0,1]); title('训练后'); %xlabel('前3个季度的用电量'); xlabel('年份'); ylabel('第四季度的用电量'); %输出权值矩阵和阈值 iw1 = net.IW{1} b1 = net.b{1} iw2 = net.LW{2} b2 = net.b{2} %Y = sim(net,data) %存储训练好的神经网络 save netkohler net