神经网络分析测误差.doc

用BP神经网络进行预测班级：信计071 姓名：郭怡志 学号0710820104用BP神经网络进行股票预测1、目的：掌握BP神经网络算法及其Matlab环境下编程实现2、题目：用BP神经网络对附表3.1的训练数据，预测附表3.2中数据的收盘价3、设计过程：用“571”BP神经网络进行训练和预测4、Matlab源程序如下：P=12.4912.4912.2112.2213607255167520944;12.2212.7212.1812.6629082744363202560;12.5812.7412.4512.4622802103286893924;12.4212.5612.2612.3 15584448193329056;12.2612.6812.2612.6719746980246494080;12.8212.9712.5512.7827583354351219296;12.7713.4212.6613.3643462230570755526; 13.0113.37 13.0113.1931144350 410931872;13.1613.5913.1413.4323668448316467424;13.4913.5 13.1213.1919269676255614928;13.1913.7213.0213.6 36358424487460544;13.6 13.7313.5113.6223845804324467584;13.6213.7112.9512.9922462342299506784;12.9913.1212.7512.8920363860263010736;12.7 12.8312.2 12.5317330308217278656;12.4 12.5712.3112.417140875 88986592;12.4912.5512 12.0410509253128913712;12.0912.2511.9712.017511755 90902304;12.0212.1811.9612.157514297 90803536;12.1 12.3212.0312.147283098 88802800; 12.1 12.15 11.58 11.7111352825 133326640;11.7311.9211.5711.597573305 89033640;11.6711.9 11.2111.8910277141119762592; 11.76 11.89 11.61 11.73 11368580 133410400;11.4412.6511.3612.1135556024430798464;12.0912.2911.6711.9313918940166163120;11.9312.0211.7511.8710202644121107256;12 12.0211.7911.9 8337176 98893768;11.9 11.9611.8211.896640187 78795592;11.9111.9511.8411.936213950 73982808;11.9 12.2 11.8511.9810342758124693128;11.9812.0211.7111.966952637 82441696;11.8512.2 11.8112.199931446 119920744;12.1912.4912.1512.4116455316203530048;12.38 12.55 12.27 12.34 11542922 143000448;12.3412.7712.3212.6617138950216009424;12.6712.7112.4212.5914056620176113952;12.6 12.6312.4512.6211645531146168336;12.6 12.9712.2212.2821390096270573504;12.2812.6412.2412.4414494819180426080;12.4712.5212.3 12.3710144725125570008;12.3 12.3512.0512.269879639 120649136;12.2512.3612 12.049657725 117189536;12.1 12.1611.8711.967706144 92401216;12 12.0411.7811.798360817 99209584;11.7811.8711.2911.4519292420221751744;11.4911.5411.3 11.537994094 91515968;11.5511.7811.4811.7712833044149231184;11.7611.8311.6611.728246960 96896400;11.7611.8511.6411.816818095 80201072;11.8 11.9111.7511.876776051 80297552;11.8511.8911.6211.787782495 91447728;11.8211.8311.6611.696816861 79911968;11.6911.6911.4511.467720525 88956312;11.4711.6911.4511.635151524 59730096;11.6911.8911.6911.8 13983169165247696;11.8 11.8811.7611.8311766064 138909840;for i=1:5 P(i,:)=(p(i,:)-min(p(i,:)/(max(p(i,:)-min(p(i,:);end用BP神经网络进行预测t=12.6612.4612.312.6712.7813.3613.1913.4313.1913.613.6212.9912.8912.5312.4112.0412.0112.1512.1411.7111.5911.8911.7312.1111.9311.8711.911.8911.9311.9811.9612.1912.4112.3412.6612.5912.6212.2812.4412.3712.2612.0411.9611.7911.4511.5311.7711.7211.8111.8711.7811.6911.4611.6311.811.8311.82;for i=1:57 T(1,i)=(t(1,i)-min(t)/(max(t)-min(t);endthreshold=0 1;0 1;0 1;0 1;0 1;net=newff(threshold,7,1,tansig,logsig,trainlm);net=train(net,P,T);y_test=sim(net,P)Y_test=y_test*(max(t)-min(t)+min(t);Y_testP_test=11.7511.8511.7 11.821891623422301808011.8411.9711.7911.962592609230738889611.9711.9811.8311.931733243620624718411.911.9 11.7511.8 1609046519022611211.7611.9911.7511.9 2587263230729481611.8911.9311.7711.842232979226437366411.8611.8911.6411.671926808422625366411.7111.8811.5811.832229246826244881611.8312 11.8 11.993313740239403286411.9411.9411.6 11.741915308422611612811.7411.7711.5411.6 123394115511.5510.9110.941945713021828193610.9711.0910.7410.9111189915 12160092010.9411.0810.8211 121210001327380721111 10.7610.831317705014273507210.8110.9110.5610.671093108811722305611.8411.8611.6611.718286360 97152416;for i=1:5 P_test(i,:)=(p_test(i,:)-min(p_test(i,:)/(max(p_test(i,:)-min(p_test(i,:);endformat long ;y=sim(net,P_test)Y=y*(max(t)-min(t)+min(t);Y figure;plot(1:57,t,k*);title(预测误差（）);hold on;plot(1:57,Y_test,bo);title(预测误差（）);hold off;W=11.8211.9611.9311.811.911.8411.6711.8311.9911.7411.610.9410.911110.8310.6711.71;figure;plot(1:17,W,k*);title(预测误差（）);hold on;plot(1:17,Y,bo);title(预测误差（）);hold off;BP神经网络训练运行结果如图31：图31 BP神经网络训练效果BP神经网络对训练样本的预测结果与实际结果比较见图32：图32 BP神经网络对训练样本的预测误差 *代表实际值 o代表预测值由图32可以看出，BP神经网络对训练样本的预测精度很高，从表31中也可看出精度很高：表31 训练样本的预测精度序号收盘价预测值误差112.2212.29763217-0.07763217212.6612.658856010.001143986312.4612.46283617-0.00283617412.312.295134070.004865926512.6712.71289171-0.04289171612.7812.777093250.00290675713.3613.359892410.000107585813.1913.19060209-0.00060209913.4313.43261436-0.002614361013.1913.189144280.0008557241113.613.591950550.0080494481213.6213.619946525.34764E-051312.9912.99103834-0.001038341412.8912.8936052-0.00360521512.5312.528777330.0012226691612.4112.409260150.0007398521712.0412.033939180.0060608181812.0112.07936773-0.069367731912.1512.083567820.0664321762012.1412.14121235-0.001212352111.7111.696437840.0135621612211.5911.7566428-0.16664282311.8911.89066117-0.000661172411.7311.727725230.0022747732512.1112.109707710.000292292611.9311.923868490.0061315062711.8711.837798270.0322017272811.911.90164011-0.001640112911.8911.92387633-0.033876333011.9311.93705917-0.007059173111.9812.04790222-0.067902223211.9611.842826120.1171738833312.1912.124548420.0654515763412.4112.370597570.0394024283512.3412.322575570.0174244283612.6612.624489380.0355106183712.5912.586478080.0035219253812.6212.58337980.0366201983912.2812.2819888-0.00198884012.4412.47068646-0.030686464112.3712.41603301-0.046033014212.2612.15363670.1063633014312.0412.05996079-0.019960794411.9611.99437798-0.034377984511.7911.88992702-0.099927024611.4511.47496748-0.024967484711.5311.480592950.0494070454811.7711.73768070.0323193044911.7211.73221453-0.012214535011.8111.73744650.0725534985111.8711.84992290.02007715211.7811.730529180.0494708185311.6911.70480346-0.014803465411.4611.53879929-0.078799295511.6311.624840570.0051594315611.811.81097454-0.010974545711.8311.810202740.019797259BP神经网络对检验训练样本的分类结果与实际分类结果比较见图33：图33 BP神经网络对检验训练样本的预测误差 *代表实际值 o代表预测值BP神经网络对检验训练样本的预测精度如表32：表32 检验训练样本的预测精度序号实际值预测值误差精度（%）111.8213.30011603-1.4801160312.52213221211.9613.61929544-1.65