BP神经网络例二(分类).doc

例二：考虑如下花的分类数据序号萼片长度萼片宽度花瓣长度花瓣宽度类别15.13.51.40.2124.931.40.2134.73.21.30.2144.63.11.50.21553.61.40.2165.43.91.70.4174.63.41.40.31853.41.50.2194.42.91.40.21104.93.11.50.11115.43.71.50.21124.83.41.60.21134.831.40.11144.331.10.11155.841.20.21165.74.41.50.41175.43.91.30.41185.13.51.40.31195.73.81.70.31205.13.81.50.31215.43.41.70.21225.13.71.50.41234.63.610.21245.13.31.70.51254.83.41.90.2126531.60.212753.41.60.41285.23.51.50.21295.23.41.40.21304.73.21.60.21314.83.11.60.21325.43.41.50.41335.24.11.50.11345.54.21.40.21354.93.11.50.113653.21.20.21375.53.51.30.21384.93.11.50.11394.431.30.21405.13.41.50.214153.51.30.31424.52.31.30.31434.43.21.30.214453.51.60.61455.13.81.90.41464.831.40.31475.13.81.60.21484.63.21.40.21495.33.71.50.215053.31.40.215173.24.71.42526.43.24.51.52536.93.14.91.52545.52.341.32556.52.84.61.52565.72.84.51.32576.33.34.71.62584.92.43.312596.62.94.61.32605.22.73.91.4261523.512625.934.21.526362.2412646.12.94.71.42655.62.93.61.32666.73.14.41.42675.634.51.52685.82.74.112696.22.24.51.52705.62.53.91.12715.93.24.81.82726.12.841.32736.32.54.91.52746.12.84.71.22756.42.94.31.32766.634.41.42776.82.84.81.42786.7351.727962.94.51.52805.72.63.512815.52.43.81.12825.52.43.712835.82.73.91.228462.75.11.62855.434.51.528663.44.51.62876.73.14.71.52886.32.34.41.32895.634.11.32905.52.541.32915.52.64.41.22926.134.61.42935.82.641.229452.33.312955.62.74.21.32965.734.21.22975.72.94.21.32986.22.94.31.32995.12.531.121005.72.84.11.321016.33.362.531025.82.75.11.931037.135.92.131046.32.95.61.831056.535.82.231067.636.62.131074.92.54.51.731087.32.96.31.831096.72.55.81.831107.23.66.12.531116.53.25.1231126.42.75.31.931136.835.52.131145.72.55231155.82.85.12.431166.43.25.32.331176.535.51.831187.73.86.72.231197.72.66.92.3312062.251.531216.93.25.72.331225.62.84.9231237.72.86.7231246.32.74.91.831256.73.35.72.131267.23.261.831276.22.84.81.831286.134.91.831296.42.85.62.131307.235.81.631317.42.86.11.931327.93.86.4231336.42.85.62.231346.32.85.11.531356.12.65.61.431367.736.12.331376.33.45.62.431386.43.15.51.83139634.81.831406.93.15.42.131416.73.15.62.431426.93.15.12.331435.82.75.11.931446.83.25.92.331456.73.35.72.531466.735.22.331476.32.551.931486.535.2231496.23.45.42.331505.935.11.83这是一个三类问题，为了验证算法的性能，用每类的前25个数据（共75）作为训练样本，用BP神经网络进行建模，并对剩下的样本用该网络进行判别。训练样本如下：5.13.51.40.216.73.14.41.424.931.40.215.634.51.524.73.21.30.215.82.74.1124.63.11.50.216.22.24.51.5253.61.40.215.62.53.91.125.43.91.70.415.93.24.81.824.63.41.40.316.12.841.3253.41.50.216.32.54.91.524.42.91.40.216.12.84.71.224.93.11.50.116.42.94.31.325.43.71.50.216.33.362.534.83.41.60.215.82.75.11.934.831.40.117.135.92.134.331.10.116.32.95.61.835.841.20.216.535.82.235.74.41.50.417.636.62.135.43.91.30.414.92.54.51.735.13.51.40.317.32.96.31.835.73.81.70.316.72.55.81.835.13.81.50.317.23.66.12.535.43.41.70.216.53.25.1235.13.71.50.416.42.75.31.934.63.610.216.835.52.135.13.31.70.515.72.55234.83.41.90.215.82.85.12.4373.24.71.426.43.25.32.336.43.24.51.526.535.51.836.93.14.91.527.73.86.72.235.52.341.327.72.66.92.336.52.84.61.5262.251.535.72.84.51.326.93.25.72.336.33.34.71.625.62.84.9234.92.43.3127.72.86.7236.62.94.61.326.32.74.91.835.22.73.91.426.73.35.72.13523.5125.934.21.5262.24126.12.94.71.425.62.93.61.32检验样本531.60.215.52.64.41.2253.41.60.416.134.61.425.23.51.50.215.82.641.225.23.41.40.2152.33.3124.73.21.60.215.62.74.21.324.83.11.60.215.734.21.225.43.41.50.415.72.94.21.325.24.11.50.116.22.94.31.325.54.21.40.215.12.531.124.93.11.50.115.72.84.11.3253.21.20.217.23.261.835.53.51.30.216.22.84.81.834.93.11.50.116.134.91.834.431.30.216.42.85.62.135.13.41.50.217.235.81.6353.51.30.317.42.86.11.934.52.31.30.317.93.86.4234.43.21.30.216.42.85.62.2353.51.60.616.32.85.11.535.13.81.90.416.12.65.61.434.831.40.317.736.12.335.13.81.60.216.33.45.62.434.63.21.40.216.43.15.51.835.33.71.50.21634.81.8353.31.40.216.93.15.42.136.634.41.426.73.15.62.436.82.84.81.426.93.15.12.336.7351.725.82.75.11.9362.94.51.526.83.25.92.335.72.63.5126.73.35.72.535.52.43.81.126.735.22.335.52.43.7126.32.551.935.82.73.91.226.535.22362.75.11.626.23.45.42.335.434.51.525.935.11.8363.44.51.626.73.14.71.526.32.34.41.325.634.11.325.52.541.32用BP神经网络对数据进行分类源程序如下：p=5.1,3.5,1.4,0.2;4.9,3.0,1.4,0.2;4.7,3.2,1.3,0.2;4.6,3.1,1.5,0.2;5.0,3.6,1.4,0.2;5.4,3.9,1.7,0.4;4.6,3.4,1.4,0.3;5.0,3.4,1.5,0.2;4.4,2.9,1.4,0.2;4.9,3.1,1.5,0.1;5.4,3.7,1.5,0.2;4.8,3.4,1.6,0.2;4.8,3.0,1.4,0.1;4.3,3.0,1.1,0.1;5.8,4.0,1.2,0.2;5.7,4.4,1.5,0.4;5.4,3.9,1.3,0.4;5.1,3.5,1.4,0.3;5.7,3.8,1.7,0.3;5.1,3.8,1.5,0.3;5.4,3.4,1.7,0.2;5.1,3.7,1.5,0.4;4.6,3.6,1.0,0.2;5.1,3.3,1.7,0.5;4.8,3.4,1.9,0.2;7.0,3.2,4.7,1.4;6.4,3.2,4.5,1.5;6.9,3.1,4.9,1.5;5.5,2.3,4.0,1.3;6.5,2.8,4.6,1.5;5.7,2.8,4.5,1.3;6.3,3.3,4.7,1.6;4.9,2.4,3.3,1.0;6.6,2.9,4.6,1.3;5.2,2.7,3.9,1.4;5.0,2.0,3.5,1.0;5.9,3.0,4.2,1.5;6.0,2.2,4.0,1.0;6.1,2.9,4.7,1.4;5.6,2.9,3.6,1.3;6.7,3.1,4.4,1.4;5.6,3.0,4.5,1.5;5.8,2.7,4.1,1.0;6.2,2.2,4.5,1.5;5.6,2.5,3.9,1.1;5.9,3.2,4.8,1.8;6.1,2.8,4.0,1.3;6.3,2.5,4.9,1.5;6.1,2.8,4.7,1.2;6.4,2.9,4.3,1.3;6.3,3.3,6.0,2.5;5.8,2.7,5.1,1.9;7.1,3.0,5.9,2.1;6.3,2.9,5.6,1.8;6.5,3.0,5.8,2.2;7.6,3.0,6.6,2.1;4.9,2.5,4.5,1.7;7.3,2.9,6.3,1.8;6.7,2.5,5.8,1.8;7.2,3.6,6.1,2.5;6.5,3.2,5.1,2.0;6.4,2.7,5.3,1.9;6.8,3.0,5.5,2.1;5.7,2.5,5.0,2.0;5.8,2.8,5.1,2.4;6.4,3.2,5.3,2.3;6.5,3.0,5.5,1.8;7.7,3.8,6.7,2.2;7.7,2.6,6.9,2.3;6.0,2.2,5.0,1.5;6.9,3.2,5.7,2.3;5.6,2.8,4.9,2.0;7.7,2.8,6.7,2.0;6.3,2.7,4.9,1.8;6.7,3.3,5.7,2.1;for i=1:4 P(i,:)=(p(i,:)-min(p(i,:)/(max(p(i,:)-min(p(i,:);endT=100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;100;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;010;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;001;threshold=0 1;0 1;0 1;0 1;net=newff(threshold,9,3,tansig,logsig,trainlm);net=train(net,P,T);y_test=sim(net,P)p_test=5.0,3.0,1.6,0.2;5.0,3.4,1.6,0.4;5.2,3.5,1.5,0.2;5.2,3.4,1.4,0.2;4.7,3.2,1.6,0.2;4.8,3.1,1.6,0.2;5.4,3.4,1.5,0.4;5.2,4.1,1.5,0.1;5.5,4.2,1.4,0.2;4.9,3.1,1.5,0.1;5.0,3.2,1.2,0.2;5.5,3.5,1.3,0.2;4.9,3.1,1.5,0.1;4.4,3.0,1.3,0.2;5.1,3.4,1.5,0.2;5.0,3.5,1.3,0.3;4.5,2.3,1.3,0.3;4.4,3.2,1.3,0.2;5.0,3.5,1.6,0.6;5.1,3.8,1.9,0.4;4.8,3.0,1.4,0.3;5.1,3.8,1.6,0.2;4.6,3.2,1.4,0.2;5.3,3.7,1.5,0.2;5.0,3.3,1.4,0.2;6.6,3.0,4.4,1.4;6.8,2.8,4.8,1.4;6.7,3.0,5.0,1.7;6.0,2.9,4.5,1.5;5.7,2.6,3.5,1.0;5.5,2.4,3.8,1.1;5.5,2.4,3.7,1.0;5.8,2.7,3.9,1.2;6.0,2.7,5.1,1.6;5.4,3.0,4.5,1.5;6.0,3.4,4.5,1.6;6.7,3.1,4.7,1.5;6.3,2.3,4.4,1.3;5.6,3.0,4.1,1.3;5.5,2.5,4.0,1.3;5.5,2.6,4.4,1.2;6.1,3.0,4.6,1.4;5.8,2.6,4.0,1.2;5.0,2.3,3.3,1.0;5.6,2.7,4.2,1.3;5.7,3.0,4.2,1.2;5.7,2.9,4.2,1.3;6.2,2.9,4.3,1.3;5.1,2.5,3.0,1.1;5.7,2.8,4.1,1.3;7.2,3.2,6.0,1.8;6.2,2.8,4.8,1.8;6.1,3.0,4.9,1.8;6.4,2.8,5.6,2.1;7.2,3.0,5.8,1.6;7.4,2.8,6.1,1.9;7.9,3.8,6.4,2.0;6.4,2.8,5.6,2.2;6.3,2.8,5.1,1.5;6.1,2.6,5.6,1.4;7.7,3.0,6.1,2.3;6.3,3.4,5.6,2.4;6.4,3.1,5.5,1.8;6.0,3.0,4.8,1.8;6.9,3.1,5.4,2.1;6.7,3.1,5.6,2.4;6.9,3.1,5.1,2.3;5.8,2.7,5.1,1.9;6.8,3.2,5.9,2.3;6.7,3.3,5.7,2.5;6.7,3.0,5.2,2.3;6.3,2.5,5.0,1.9;6.5,3.0,5.2,2.0;6.2,3.4,5.4,2.3;5.9,3.0,5.1,1.8;for i=1:4 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)yy=y;输出结果如下表：表一训练效果序号输出结果类别10.9999999992.40292E-080120.9999999984.8321E-080130.9999999992.80884E-080140.9999999993.13649E-080150.9999999992.38828E-080160.9999999992.38627E-080170.9999999992.54809E-080180.9999999992.42581E-080190.9999999921.4174E-0701100.9999999992.64236E-0801110.9999999992.39027E-0801120.9999999992.41787E-0801130.9999999993.1823E-0801140.9999999984.07099E-0801150.9999999992.40597E-0801160.9999999992.39702E-0801170.9999999992.38998E-0801180.9999999992.44281E-0801190.9999999992.39809E-0801200.9999999992.3833E-0801210.9999999992.41763E-0801220.9999999992.40453E-0801230.9999999992.40421E-0801240.9999999964.44149E-0801250.9999999992.40184E-0801261.12615E-080.9999999241.25E-112273E-141022800.9999999613.3403E-1022900.9999996742.32481E-0823000.9999999934.1232E-102310102325E-1410233012.13E-1223400.9999999663.744E-1023500.9999999724.35287E-0923600.9999999886.8942E-1023701023800.9999999952.4677E-10239010240010241013.9E-132424E-14102431.341E-1111E-1424400.9999996322.8594E-0824500.9999999987.885E-112462E-140.9999995114.34375E-0724700.9999999991.894E-1124800.9999993046.29284E-082493.582E-1113E-1425000.9999999991.185E-1125101.15217E-07135205.7257E-10135300135408.5603E-10135500135600135701.09E-070.99999981735807.47146E-07135907.8E-13136001.90678E-07136102.88189E-07136203.22E-12136300136401.8076E-10136500136605E-14136701.06206E-070.9999999993681.22495E-093.07747E-070.99999991936900137007.10165E-070.99999849837100137202.78716E-090.99999999937301.2E-13137406.03109E-070.99999976237501.53615E-0713对训练样本的错判率为0；模型检验（用模型对剩下的样本进行判断检验）如下表：序号输出结果类别10.9999999941.23408E-070120.9999999992.85432E-080130.9999999992.40204E-080140.9999999992.46478E-080150.9999999992.96313E-080160.9999999984.43027E-080170.9999999982.96062E-080180.9999999992.38964E-080190.9999999992.39427E-0801100.9999999993.19242E-0801110.9999999973.90521E-0801120.9999999992.41842E-0801130.9999999993.19242E-0801140.9999996537.06353E-0701150.9999999992.44806E-0801160.9999999992.48784E-0801176.683E-114.6511E-104.47E-122180.9999999983.70609E-0801190.9999999934.3173E-0801200.9999999992.38157E-0801210.9998203621.0451E-0501220.9999999992.38118E-0801230.999