肾炎诊断问题——神经网络.doc

神经网络算法的matlab实现：x=16615.824.570011217951318515.731.57011251844271939.8025.954116312864215914.239.789699.223972622616.223.860615270.32181719.299.2930718745.525720113.326.655110149.414114714.530.06591021546801728.857.8655175.798.431815611.532.563910710355213215.917.757892.41314137218211.311.37671112646721869.2637.195823373.03471628.2327.162510862.44651506.6321.062714017963915910.711.761219098.539011716.17.0498895.513657218110.14.04143718410154214620.723.81232128150109242.310.39.7062993.743988828.212.453.137044.145485215413.853.362110516072317912.217.9113915045.221813.53.3616.813532.651.61821755.8424.980712355.612611315.847.362653.616862750.511.66.3060858.958.913978.614.69.7042170.813346490.03.278.1762252.377085217828.832.499211270.216921319.136.2222024940.016817013.929.8128522647.933016213.219.8152116636.213320313.090.8154416298.9039416713.114.1227821246.313416412.918.6299319736.394.516715.027.0205626064.623715814.437.0102510144.672.513322.831.01633401180899156135322674710902288101698.00308106899.153.028924717.38.65255424177.93731668.1062.812332521346492096.4386.9215728874.02191826.4961.7387043214336723515.623.4180616668.818817319.117.0249729565.828715119.764.2203140318287419165.435.0536139213768822324.486.0360335397.747922120.1155317236815073921725.028.2234337311049416422.235.522122811535491738.9936.0162421610325720218.617.7378522531.067.318217.324.8307324650.710921124.017.0383642873.535124621.593.2211235471.719516416.138.0213515264.324017921.035.0156022647.9330;x=x;save x load x y=ones(1,30) zeros(1,30) ; y = Columns 1 through 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Columns 17 through 32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 Columns 33 through 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Columns 49 through 60 0 0 0 0 0 0 0 0 0 0 0 0 a=minmax(x) ;a = 1.0e+003 * 0.0135 0.2470 0.0033 0.1350 0.0040 0.3220 0.1350 6.7470 0.0326 1.0900 0.0310 1.3140 0.0673 1.3720 net=newp(a,2) ;net = Neural Network object: architecture: numInputs: 1 numLayers: 1 biasConnect: 1 inputConnect: 1 layerConnect: 0 outputConnect: 1 targetConnect: 1 numOutputs: 1 (read-only) numTargets: 1 (read-only) numInputDelays: 0 (read-only) numLayerDelays: 0 (read-only) subobject structures: inputs: 1x1 cell of inputs layers: 1x1 cell of layers outputs: 1x1 cell containing 1 output targets: 1x1 cell containing 1 target biases: 1x1 cell containing 1 bias inputWeights: 1x1 cell containing 1 input weight layerWeights: 1x1 cell containing no layer weights functions: adaptFcn: trains initFcn: initlay performFcn: mae trainFcn: trainc parameters: adaptParam: .passes initParam: (none) performParam: (none) trainParam: .epochs, .goal, .show, .time weight and bias values: IW: 1x1 cell containing 1 input weight matrix LW: 1x1 cell containing no layer weight matrices b: 1x1 cell containing 1 bias vector other: userdata: (user stuff) net=train(net,x,y); ;? Error using = network.trainTargets are incorrectly sized for network.Matrix must have 2 rows. x1=58.25.4229.73231381795131061.8740.55421771844271520.8012.5133217612864685.51.703.9950362.3238762.61440.7015.154779.771.0218.585.71.094.279017045.8257.91440.309.1141755249.5141.51704.169.32943260155680.81760.5727.331813399.4318.81927.0632.919693431035531888.2822.61208231131413721535.8734.8328163264672.51432.8415.726512373.0347.521319.136.2222024962.0465.819220.123.8160615640.016817110.530.567214547.0330.516213.219.8152116636.213320313.090.8154416298.9394.516420.128.9106216147.3134.516713.114.1227821236.596.516412.918.6299319765.5237.816715.027.0205626044.872.015814.437.01025101180899.513322.831.316334012282891698.030.8106899.153.081724717.38.65255424177.5373.51853.9031.31211190134649.82096.4386.9215728874.0219.81826.