数据挖掘题目及答案.doc

数据挖掘课程设计作业：学号11. “book11”中的数据是股票“中金黄金”的历史交易数据，请根据此训练样本，建立BP神经网络预测模型，并预测“book12”中数据对应的“第二个交易日收盘价”。2.“book21”中的数据是三种等级的葡萄酒检验数据，请根据此训练样本，建立BP神经网络分类模型，并预测“book22”中数据对应的葡萄酒的等级（class）。3. 用支持向量机方法根据“book31”中训练样本建立工艺参数与“透气量”之间的预测模型，并预测“book32”数据中对应的“透气量”。4. “book41”中的数据是肿瘤的检验数据，最后一列是肿瘤的性质（B：良性，M恶性），请根据此训练样本，建立BP神经网络分类模型，并预测“book42”中数据对应的肿瘤性质。5.“book51”是二分类训练样本，“book52”是待分类样本，请完成如下数据挖掘的分类任务：1）按5-最邻近，判别“book52”中待分类样本的类别。2）按3-最邻近，同时使用距离加权方法，判别“book52”中待分类样本的类别。3）按1、3、5-最邻近，再使用二次表决方法，判别“book52”中待分类样本的类别。第一题 源程序p=40.58 41 40.4 40.21 20370030 82617369638.99 39.1 38.89 38.44 25964924 100565958438.51 38.9 38.11 38.01 17117204 65638854438.08 38.08 37.7 37.43 17311410 65236300837.9 38.15 37.79 37.38 11947145 45127142437.79 37.82 37.76 37.01 9294011 34787465637.95 38.1 37.8 37.4 8537574 32196275237.91 37.99 37.36 37 10316651 38615526436.99 37 35.86 35.78 15193152 55054406435.28 35.5 34.11 33.8 16117000 55812947234.17 34.8 34.74 33.81 8192188 28016038434.11 35.52 35.28 34.11 9424522 32936726434.99 34.99 33.56 33.5 13075993 44596633633.09 33.51 33.1 32.3 12000334 39607078432.5 32.5 32.28 31.1 11682467 37104105632 32.55 32.5 31.88 6026622 19474633632.57 34.08 33.95 32.34 12843017 43032192033 33.87 33.63 33 7936495 26504041634.2 35.55 35.33 33.99 14601733 50836681635.33 35.33 35.05 34.78 10207492 35747641635.21 35.87 34.27 34.02 11016954 38651529634.3 34.49 34.47 33.28 8344432 28340601634.49 34.86 34.44 34.01 7652904 26371872034.2 35.59 35.39 34.18 17463280 61302688035.5 36.49 35.66 35.45 20956380 75420300835.75 36.29 36.14 35.45 15156678 54481171236.2 36.45 35.97 35.7 13648551 49164860836.25 37.19 36.22 36.11 18542714 67826643236.23 36.9 36.67 36.23 13507372 49399788837.38 38.29 36.83 36.7 32383364 121488870436.86 39 38.6 36.86 37287772 142752486439.1 39.38 38.09 37.8 22396188 85844441637.7 38.2 37.75 36.67 16913584 63262918437.9 37.99 37.71 37.02 11454268 42926092837.66 38 37.96 37.16 15508051 58140160038.5 39.37 38.74 37.78 32792106 126679731238.72 38.72 37.38 37.35 19419540 73964243237.01 37.55 37.34 36.85 10311362 38300291237.99 38.39 38.18 37.56 15262721 58113068838.2 38.4 38.01 37.87 12773053 48671971238 38 37.64 37.46 13447938 50563718437.6 37.6 37.08 37 10965149 40724380836.66 37.36 37.14 36.45 15172980 55727808037.2 37.58 36.91 36.81 9592439 35563520036.69 36.7 35.73 35.3 16175220 57930016035.3 36.3 36.13 35.2 14059946 50318864035.5 36.09 35.36 35.3 9763086 34718336035.94 36 35.54 35.45 8492061 30266214436.18 36.46 36.35 36.02 12588354 45683721636.51 37.36 37.09 36.11 16738276 61663878437.09 37.23 36.99 36.68 12502770 46206956837.4 37.88 37.1 37.06 13466983 50416995236.89 38.97 38.39 36.82 39996664 152857305638.29 38.3 37.63 37.42 25430348 96294451237.28 37.28 36.42 36.4 18391436 67565811236.4 37.1 36.89 36.3 11572464 42446806436.91 37.38 36.46 36.16 8807557 32250016036.9 37.36 36.97 36.38 9777501 36072320037.5 37.96 37.59 37.12 19098062 717384704;for i=1:6 P(i,:)=(p(i,:)-min(p(i,:)/(max(p(i,:)-min(p(i,:);endt=38.8938.1137.737.7937.7637.837.3635.8634.1134.7435.2833.5633.132.2832.533.9533.6335.3335.0534.2734.4734.4435.3935.6636.1435.9736.2236.6736.8338.638.0937.7537.7137.9638.7437.3837.3438.1838.0137.6437.0837.1436.9135.7336.1335.3635.5436.3537.0936.9937.138.3937.6336.4236.8936.4636.9737.5937.48;A=(t-min(t)/(max(t)-min(t);p_test=37.8 38.15 37.48 37.45 15218901 57430515237.51 38 37.93 37.25 18769714 70850432038.31 38.55 37.72 37.61 18137700 68983232037.4 37.4 36.89 36.54 14130081 52215888036.7 37.18 37.14 36.41 8545191 31482915236.99 37.4 37.1 36.65 10064691 37206800037.5 37.85 37.27 37.08 10755931 40226908837.49 38.25 37.74 37.49 22166932 83867603237.96 39.97 39.57 37.5 54384640 212207449640 40.49 39.29 39.11 35051836 138899609639.61 41.3 41.04 39.4 54397576 219896934440.95 40.95 39.24 39.11 37098496 147853171238.25 38.52 37.55 37.42 21549920 81538617637.85 38.08 37.04 36.8 16274166 60677305637.81 37.98 36.93 36.9 12557125 46945254437.28 37.44 37.4 36.96 8349168 31108582437 37 36.15 35.21 16450156 59198156835.6 35.79 34.6 34.6 16102593 56572902434.02 35.02 34.45 34.02 10358778 35671542433.6 33.89 33.41 32.8 15288490 50784105633.55 34.22 33.97 33.3 8563277 28943920034.1 34.5 34.48 34.1 8209304 28213779234.78 34.79 34.39 34.28 5856122 20199371233.8 33.8 33.09 32.99 11085069 36996000033.27 33.58 33.54 33 6619196 220021632;for i=1:6P_test(i,:)=(p_test(i,:)-min(p_test(i,:)/(max(p_test(i,:)-min(p_test(i,:);endnet=newff(minmax(P),6,1,tansig,logsig);net.trainParam.epochs=2000;net.trainParam.goal=0.001;LP.lr=0.1;net=init(net);net=train(net,P,A);temp=sim(net,P);temp1=sim(net,P_test);yuce=temp*(max(t)-min(t)+min(t)yuce1=temp1*(max(t)-min(t)+min(t)运行结果yuce = Columns 1 through 8 38.8900 38.2915 38.3535 38.2041 37.9346 37.7946 37.3593 35.9601 Columns 9 through 16 34.5224 34.6301 35.2810 33.7284 33.0996 32.7586 32.6000 33.8718 Columns 17 through 24 33.3900 35.3054 35.4152 34.4241 34.4626 34.4377 35.4777 35.7399 Columns 25 through 32 36.5976 35.2247 36.0796 36.7377 36.5838 37.8040 38.0883 37.7076 Columns 33 through 40 37.7209 37.9124 38.3069 37.4442 37.1811 38.1744 38.3685 37.9466 Columns 41 through 48 37.8554 36.4487 36.7846 35.9870 35.8927 35.4955 35.5355 36.3506 Columns 49 through 56 36.7087 36.9675 37.3913 37.6803 37.5722 37.0468 37.1114 36.8054 Columns 57 through 59 36.9708 37.2478 36.9716yuce1 = Columns 1 through 8 38.3830 38.6225 38.5678 37.9764 37.4559 37.7042 37.7073 38.7068 Columns 9 through 16 38.7539 38.8307 38.8900 38.7801 38.6973 38.2571 36.0091 37.2707 Columns 17 through 24 37.0558 34.3896 36.9194 32.9965 35.3717 35.2927 35.3337 35.0161 Column 25 34.2919第二题p=14.23 1.71 2.43 15.6 127 2.8 3.06 0.28 2.29 5.64 1.04 3.92 106513.2 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.4 105013.16 2.36 2.67 18.6 101 2.8 3.24 0.3 2.81 5.68 1.03 3.17 118514.37 1.95 2.5 16.8 113 3.85 3.49 0.24 2.18 7.8 0.86 3.45 148013.24 2.59 2.87 21 118 2.8 2.69 0.39 1.82 4.32 1.04 2.93 73514.2 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 145014.39 1.87 2.45 14.6 96 2.5 2.52 0.3 1.98 5.25 1.02 3.58 129014.06 2.15 2.61 17.6 121 2.6 2.51 0.31 1.25 5.05 1.06 3.58 129514.83 1.64 2.17 14 97 2.8 2.98 0.29 1.98 5.2 1.08 2.85 104513.86 1.35 2.27 16 98 2.98 3.15 0.22 1.85 7.22 1.01 3.55 104514.1 2.16 2.3 18 105 2.95 3.32 0.22 2.38 5.75 1.25 3.17 151014.12 1.48 2.32 16.8 95 2.2 2.43 0.26 1.57 5 1.17 2.82 128013.75 1.73 2.41 16 89 2.6 2.76 0.29 1.81 5.6 1.15 2.9 132014.38 1.87 2.38 12 102 3.3 3.64 0.29 2.96 7.5 1.2 3 154713.63 1.81 2.7 17.2 112 2.85 2.91 0.3 1.46 7.3 1.28 2.88 131014.3 1.92 2.72 20 120 2.8 3.14 0.33 1.97 6.2 1.07 2.65 128013.83 1.57 2.62 20 115 2.95 3.4 0.4 1.72 6.6 1.13 2.57 113014.19 1.59 2.48 16.5 108 3.3 3.93 0.32 1.86 8.7 1.23 2.82 168013.64 3.1 2.56 15.2 116 2.7 3.03 0.17 1.66 5.1 0.96 3.36 84514.75 1.73 2.39 11.4 91 3.1 3.69 0.43 2.81 5.4 1.25 2.73 115012.37 0.94 1.36 10.6 88 1.98 0.57 0.28 0.42 1.95 1.05 1.82 52012.33 1.1 2.28 16 101 2.05 1.09 0.63 0.41 3.27 1.25 1.67 68012.64 1.36 2.02 16.8 100 2.02 1.41 0.53 0.62 5.75 0.98 1.59 45013.67 1.25 1.92 18 94 2.1 1.79 0.32 0.73 3.8 1.23 2.46 63012.37 1.13 2.16 19 87 3.5 3.1 0.19 1.87 4.45 1.22 2.87 42012.17 1.45 2.53 19 104 1.89 1.75 0.45 1.03 2.95 1.45 2.23 35512.37 1.21 2.56 18.1 98 2.42 2.65 0.37 2.08 4.6 1.19 2.3 67813.11 1.01 1.7 15 78 2.98 3.18 0.26 2.28 5.3 1.12 3.18 50212.37 1.17 1.92 19.6 78 2.11 2 0.27 1.04 4.68 1.12 3.48 51013.34 0.94 2.36 17 110 2.53 1.3 0.55 0.42 3.17 1.02 1.93 75012.21 1.19 1.75 16.8 151 1.85 1.28 0.14 2.5 2.85 1.28 3.07 71812.29 1.61 2.21 20.4 103 1.1 1.02 0.37 1.46 3.05 0.906 1.82 87013.86 1.51 2.67 25 86 2.95 2.86 0.21 1.87 3.38 1.36 3.16 41013.49 1.66 2.24 24 87 1.88 1.84 0.27 1.03 3.74 0.98 2.78 47212.99 1.67 2.6 30 139 3.3 2.89 0.21 1.96 3.35 1.31 3.5 98511.96 1.09 2.3 21 101 3.38 2.14 0.13 1.65 3.21 0.99 3.13 88611.66 1.88 1.92 16 97 1.61 1.57 0.34 1.15 3.8 1.23 2.14 42813.03 0.9 1.71 16 86 1.95 2.03 0.24 1.46 4.6 1.19 2.48 39211.84 2.89 2.23 18 112 1.72 1.32 0.43 0.95 2.65 0.96 2.52 50012.33 0.99 1.95 14.8 136 1.9 1.85 0.35 2.76 3.4 1.06 2.31 75012.86 1.35 2.32 18 122 1.51 1.25 0.21 0.94 4.1 0.76 1.29 63012.88 2.99 2.4 20 104 1.3 1.22 0.24 0.83 5.4 0.74 1.42 53012.81 2.31 2.4 24 98 1.15 1.09 0.27 0.83 5.7 0.66 1.36 56012.7 3.55 2.36 21.5 106 1.7 1.2 0.17 0.84 5 0.78 1.29 60012.51 1.24 2.25 17.5 85 2 0.58 0.6 1.25 5.45 0.75 1.51 65012.6 2.46 2.2 18.5 94 1.62 0.66 0.63 0.94 7.1 0.73 1.58 69512.25 4.72 2.54 21 89 1.38 0.47 0.53 0.8 3.85 0.75 1.27 72012.53 5.51 2.64 25 96 1.79 0.6 0.63 1.1 5 0.82 1.69 51513.49 3.59 2.19 19.5 88 1.62 0.48 0.58 0.88 5.7 0.81 1.82 58012.84 2.96 2.61 24 101 2.32 0.6 0.53 0.81 4.92 0.89 2.15 59012.93 2.81 2.7 21 96 1.54 0.5 0.53 0.75 4.6 0.77 2.31 60013.36 2.56 2.35 20 89 1.4 0.5 0.37 0.64 5.6 0.7 2.47 78013.52 3.17 2.72 23.5 97 1.55 0.52 0.5 0.55 4.35 0.89 2.06 52013.62 4.95 2.35 20 92 2 0.8 0.47 1.02 4.4 0.91 2.05 55012.25 3.88 2.2 18.5 112 1.38 0.78 0.29 1.14 8.21 0.65 2 85513.16 3.57 2.15 21 102 1.5 0.55 0.43 1.3 4 0.6 1.68 83013.88 5.04 2.23 20 80 0.98 0.34 0.4 0.68 4.9 0.58 1.33 41512.87 4.61 2.48 21.5 86 1.7 0.65 0.47 0.86 7.65 0.54 1.86 62513.32 3.24 2.38 21.5 92 1.93 0.76 0.45 1.25 8.42 0.55 1.62 65013.08 3.9 2.36 21.5 113 1.41 1.39 0.34 1.14 9.4 0.57 1.33 550;for i=1:13 P(i,:)=(p(i,:)-min(p(i,:)/(max(p(i,:)-min(p(i,:);endT=1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;1 0 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 1 0;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;0 0 1;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,9,3,tansig,logsig,trainlm);net=train(net,P,T);y_test=sim(net,P)p_test=14.06 1.63 2.28 16 126 3 3.17 0.24 2.1 5.65 1.09 3.71 78012.93 3.8 2.65 18.6 102 2.41 2.41 0.25 1.98 4.5 1.03 3.52 77013.71 1.86 2.36 16.6 101 2.61 2.88 0.27 1.69 3.8 1.11 4 103512.85 1.6 2.52 17.8 95 2.48 2.37 0.26 1.46 3.93 1.09 3.63 101513.5 1.81 2.61 20 96 2.53 2.61 0.28 1.66 3.52 1.12 3.82 84513.05 2.05 3.22 25 124 2.63 2.68 0.47 1.92 3.58 1.13 3.2 83013.39 1.77 2.62 16.1 93 2.85 2.94 0.34 1.45 4.8 0.92 3.22 119513.3 1.72 2.14 17 94 2.4 2.19 0.27 1.35 3.95 1.02 2.77 128513.87 1.9 2.8 19.4 107 2.95 2.97 0.37 1.76 4.5 1.25 3.4 91514.02 1.68 2.21 16 96 2.65 2.33 0.26 1.98 4.7 1.04 3.59 103513.73 1.5 2.7 22.5 101 3 3.25 0.29 2.38 5.7 1.19 2.71 128513.58 1.66 2.36 19.1 106 2.86 3.19 0.22 1.95 6.9 1.09 2.88 151513.68 1.83 2.36 17.2 104 2.42 2.69 0.42 1.97 3.84 1.23 2.87 99013.76 1.53 2.7 19.5 132 2.95 2.74 0.5 1.35 5.4 1.25 3 123513.51 1.8 2.65 19 110 2.35 2.53 0.29 1.54 4.2 1.1 2.87 109513.48 1.81 2.41 20.5 100 2.7 2.98 0.26 1.86 5.1 1.04 3.47 92013.28 1.64 2.84 15.5 110 2.6 2.68 0.34 1.36 4.6 1.09 2.78 88013.05 1.65 2.55 18 98 2.45 2.43 0.29 1.44 4.25 1.12 2.51 110513.07 1.5 2.1 15.5 98 2.4 2.64 0.28 1.37 3.7 1.18 2.69 102014.22 3.99 2.51 13.2 128 3 3.04 0.2 2.08 5.1 0.89 3.53 76013.56 1.71 2.31 16.2 117 3.15 3.29 0.34 2.34 6.13 0.95 3.38 79513.41 3.84 2.12 18.8 90 2.45 2.68 0.27 1.48 4.28 0.91 3 103513.88 1.89 2.59 15 101 3.25 3.56 0.17 1.7 5.43 0.88 3.56 109513.24 3.98 2.29 17.5 103 2.64 2.63 0.32 1.66 4.36 0.82 3 68013.05 1.77 2.1 17 107 3 3 0.28 2.03 5.04 0.88 3.35 88514.21 4.04 2.44 18.9 111 2.85 2.65 0.3 1.25 5.24 0.87 3.33 108014.38 3.59 2.28 16 102 3.25 3.17 0.27 2.19 4.9 1.04 3.44 106513.9 1.68 2.12 16 101 3.1 3.39 0.21 2.14 6.1 0.91 3.33 98514.1 2.02 2.4 18.8 103 2.75 2.92 0.32 2.38 6.2 1.07 2.75 106013.94 1.73 2.27 17.4 108 2.88 3.54 0.32 2.08 8.9 1.12 3.1 126013.05 1.73 2.04 12.4 92 2.72 3.27 0.17 2.91 7.2 1.12 2.91 115013.83 1.65 2.6 17.2 94 2.45 2.99 0.22 2.29 5.6 1.24 3.37 126513.82 1.75 2.42 14 111 3.88 3.74 0.32 1.87 7.05 1.01 3.26 119013.77 1.9 2.68 17.1 115 3 2.79 0.39 1.68 6.3 1.13 2.93 137513.74 1.67 2.25 16.4 118 2.6 2.9 0.21 1.62 5.85 0.92 3.2 106013.56 1.73 2.46 20.5 116 2.96 2.78 0.2 2.45 6.25 0.98 3.03 112014.22 1.7 2.3 16.3 118 3.2 3 0.26 2.03 6.38 0.94 3.31 97013.29 1.97 2.68 16.8 102 3 3.23 0.31 1.66 6 1.07 2.84 127013.72 1.43 2.5 16.7 108 3.4 3.67 0.19 2.04 6.8 0.89 2.87 128512.7 3.87 2.4 23 101 2.83 2.55 0.43 1.95 2.57 1.19 3.13 46312 0.92 2 19 86 2.42 2.26 0.3 1.43 2.5 1.38 3.12 27812.72 1.81 2.2 18.8 86 2.2 2.53 0.26 1.77 3.9 1.16 3.14 71412.08 1.13 2.51 24 78 2 1.58 0.4 1.4 2.2 1.31 2.72 63013.05 3.86 2.32 22.5 85 1.65 1.59 0.61 1.62 4.8 0.84 2.01 51511.84 0.89 2.58 18 94 2.2 2.21 0.22 2.35 3.05 0.79 3.08 52012.67 0.98 2.24 18 99 2.2 1.94 0.3 1.46 2.62 1.23 3.16 45012.16 1.61 2.31 22.8 90 1.78 1.69 0.43 1.56 2.45 1.33 2.26 49511.65 1.67 2.62 26 88 1.92 1.61 0.4 1.34 2.6 1.36 3.21 56211.64 2.06 2.46 21.6 84 1.95 1.69 0.48 1.35 2.8 1 2.75 68012.08 1.33 2.3 23.6 70 2.2 1.59 0.42 1.38 1.74 1.07 3.21 62512.08 1.83 2.32 18.5 81 1.6 1.5 0.52 1.64 2.4 1.08 2.27 48012 1.51 2.42 22 86 1.45 1.25 0.5 1.63 3.6 1.05 2.65 45012.69 1.53 2.26 20.7 80 1.38 1.46 0.58 1.62 3.05 0.96 2.06 49512.29 2.83 2.22 18 88 2.45 2.25 0.25 1.99 2.15 1.15 3.3 29011.62 1.99 2.28 18 98 3.02 2.26 0.17 1.35 3.25 1.16 2.96 34512.47 1.52 2.2 19 162 2.5 2.27 0.32 3.28 2.6 1.16 2.63 93711.81 2.12 2.74 21.5 134 1.6 0.99 0.14 1.56 2.5 0.95 2.26 62512.29 1.41 1.98 16 85 2.55 2.5 0.29 1.77 2.9 1.23 2.74 42812.37 1.07 2.1 18.5 88 3.52 3.75 0.24 1.95 4.5 1.04 2.77 66012.29 3.17 2.21 18 88 2.85 2.99 0.45 2.81 2.3 1.42 2.83 40612.08 2.08 1.7 17.5 97 2.23 2.17 0.26 1.4 3.3 1.27 2.96 71012.6 1.34 1.9 18.5 88 1.45 1.36 0.29 1.35 2.45 1.04 2.77 56212.34 2.45 2.46 21 98 2.56 2.11 0.34 1.31 2.8 0.8 3.38 43811.82 1.72 1.88 19.5 86 2.5 1.64 0.37 1.42 2.06 0.94 2.44 41512.51 1.73 1.98 20.5 85 2.2 1.92 0.32 1.48 2.94 1.04 3.57 67212.42 2.55 2.27 22 90 1.68 1.84 0.66 1.42 2.7 0.86 3.3 31512.25 1.73 2.12 19 80 1.65 2.03 0.37 1.63 3.4 1 3.17 51012.72 1.75 2.28 22.5 84 1.38 1.76 0.48 1.63 3.3 0.88 2.42 48812.22 1.29 1.94 19 92 2.36 2.04 0.39 2.08 2.7 0.86 3.02 31211.61 1.35 2.7 20 94 2.74 2.92 0.29 2.49 2.65 0.96 3.26 68011.46 3.74 1.82 19.5 107 3.18 2.58 0.24 3.58 2.9 0.75 2.81 56212.52 2.43 2.17 21 88 2.55 2.27 0.26 1.22 2 0.9 2.78 32511.76 2.68 2.92 20 103 1.75 2.03 0.6 1.05 3.8 1.23 2.5 60711.41 0.74 2.5 21 88 2.48 2.01 0.42 1.44 3.08 1.1 2.31 43412.08 1.39 2.5 22.5 84 2.56 2.29 0.43 1.04 2.9 0.93 3.19 38511.03 1.51 2.2 21.5 85 2.46 2.17 0.52 2.01 1.9 1.71 2.87 40711.82 1.47 1.99 20.8 86 1.98 1.6 0.3 1.53 1.95 0.95 3.33 49512.42 1.61 2.19 22.5 108 2 2.09 0.34 1.61 2.06 1.06 2.96 34512.77 3.43 1.98 16 80 1.63 1.25 0.43 0.83 3.4 0.7 2.12 37212 3.43 2 19 87 2 1.64 0.37 1.87 1.28 0.93 3.05 56411.45 2.4 2.42 20 96 2.9 2.79 0.32 1.83 3.25 0.8 3.39 62511.56 2.05 3.23 28.5 119 3.18 5.08 0.47 1.87 6 0.93 3.69 46512.42 4.43 2.73 26.5 102 2.2 2.13 0.43 1.71 2.08 0.92 3.12 36513.05 5.8 2.13 21.5 86 2.62 2.65 0.3 2.01 2.6 0.73 3.1 38011.87 4.31 2.39 21 82 2.86 3.03 0.21 2.91 2.8 0.75 3.64 38012.07 2.16 2.17 21 85 2.6 2.65 0.37 1.35 2.76 0.86 3.28 37812.43 1.53 2.29 21.5 86 2.74 3.15 0.39 1.77 3.94 0.69 2.84 35211.79 2.13 2.78 28.5 92 2.13 2.24 0.58 1.76 3 0.97 2.44 46612.37 1.63 2.3 24.5 88 2.22 2.45 0.4 1.9 2.12 0.89 2.78 34212.04 4.3 2.38 22 80 2.1 1.75 0.42 1.35 2.6 0.79 2.57 58013.5 3.12 2.62 24 123 1.4 1.57 0.22 1.25 8.6 0.59 1.3 50012.79 2.67 2.48 22 112 1.48 1.36 0.24 1.26 10.8 0.48 1.47 48013.11 1.9 2.75 25.5 116 2.2 1.28 0.26 1.56 7.1 0.61 1.33 42513.23 3.3 2.28 18.5 98 1.8 0.83 0.61 1.87 10.52 0.56 1.51 67512.58 1.29 2.1 20 103 1.48 0.58 0.53 1.4 7.6 0.58 1.55 64013.17 5.19 2.32 22 93 1.74 0.63 0.61 1.55 7.9 0.6 1.48 72513.84 4.12 2.38 19.5 89 1.8 0.83 0.48 1.56 9.01 0.57 1.64 48012.45 3.03 2.64 27 97 1.9 0.58 0.63 1.14 7.5 0.67 1.73 88014.34 1.68 2.7 25 98 2.8 1.31 0.53 2.7 13 0.57 1.96 66013.48 1.67 2.64 22.5 89 2.6 1.1 0.52 2.29 11.75 0.57 1.78 62012.36 3.83 2.38 21 88 2.3 0.92 0.5 1.04 7.65 0.56 1.58 52013.69 3.26 2.54 20 107 1.83 0.56 0.5 0.8 5.88 0.96 1.82 68012.85 3.27 2.58 22 106 1.65 0.6 0.6 0.96 5.58 0.87 2.11 57012.96 3.45 2.35 18.5 106 1.39 0.7 0.4 0.94 5.28 0.68 1.75 67513.78 2.76 2.3 22 90 1.35 0.68 0.41 1.03 9.58 0.7 1.68 61513.73 4.36 2.26 22.5 88 1.28 0.47 0.52 1.15 6.62 0.78 1.75 52013.45 3.7 2.6 23 111 1.7 0.92 0.43 1.46 10.68 0.85 1.56 69512.82 3.37 2.3 19.5 88 1.48 0.66 0.4 0.97 10.26 0.72 1.75 68513.58 2.58 2.69 24.5 105 1.55 0.84 0.39 1.54 8.66 0.74 1.8 75013.4 4.6 2.86 25 112 1.98 0.96 0.27 1.11 8.5 0.67 1.92 63012.2 3.03 2.32 19 96 1.25 0.49 0.4 0.73 5.5 0.66 1.83 51012.77 2.39 2.28 19.5 86 1.39 0.51 0.48 0.64 9.899999 0.57 1.63 47014.16 2.51 2.48 20 91 1.68 0.7 0.44 1.24 9.7 0.62 1.71 66013.71 5.65 2.45 20.5 95 1.68 0.61 0.52 1.06 7.7 0.64 1.74 74013.4 3.91 2.48 23 102 1.8 0.75 0.43 1.41 7.3 0.7 1.56 75013.27 4.28 2.26 20 120 1.59 0.69 0.43 1.35 10.2 0.59 1.56 83513.17 2.59 2.37 20 120 1.65 0.68 0.53 1.46 9.3 0.6 1.62 84014.13 4.1 2.74 24.5 96 2.05 0.76 0.56 1.35 9.2 0.61 1.6 560;for i=1:13 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;运行结果y_test =0.9997029067301370.0007076209650890.00001890363764310.9994167554050960.0012713774933710.00001006196985210.9995605868444820.0008723611523980.00001827192107610.9996650633394910.0005362503905450.00002244779804510.9989397816456380.0009059524844060.00016265570742110.9995319538957120.0006248006653490.00001269186808910.9995499741949970.0006362668312650.00001342290964110.9997852141885180.0008287372954050.00003206003664210.9994254699989070.0006725701770360.00001333822788010.9995113406702410.0005897807563150.00003046881917610.9994957865876540.0007469597784890.00001047348676410.9991360340307140.0013557643010190.00001198394662610.9992465405701970.0009037731865060.00001218765629210.9994495422762570.0006162757222680.00000797136196510.9994439835761740.0006329681792810.00003090680544510.9996701060510620.0006243578757530.00003000999038310.9987826760063940.0012166476408420.00004400021912810.9994708164052230.0006912951641540.00000971974098510.9998720962743860.0006410054839290.00006164821555210.9993922466331780.0006713406867560.00000716593593410.0000007344044290.9995631316271050.00017197272429820.0000002217424680.9984151608009460.00108218962951120.0000005554982470.9987525223798440.00128254386035420.0000171389659110.9997221934204400.00000872091812020.0000032603920340.9992745479963030.00010765581164920.0000010211234940.9992902738354690.00009436496126020.0001147212721130.9991006231609180.000001542636769