基于MATLAB神经网络图像识别的高识别率代码

I0=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/0 (1).png'));I1=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/1 (1).png'));I2=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/2 (1).png'));I3=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/3 (1).png'));I4=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/4 (1).png'));I5=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/5 (1).png'));I6=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/6 (1).png'));I7=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/7 (1).png'));I8=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/8 (1).png'));I9=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/9 (1).png'));%以上数据都是归一化好的数据。P=[I0',I1',I2',I3',I4',I5',I6',I7',I8',I9'];T=eye(10,10);%%bp神经网络参数设置net=newff(minmax(P),[144,200,10],{'logsig','logsig','logsig'},'trainrp');net.inputWeights{1,1}.initFcn ='randnr';net.layerWeights{2,1}.initFcn ='randnr';net.trainparam.epochs=5000;net.trainparam.show=50;net.trainparam.lr=0.001;net.trainparam.goal=0.0000000000001;net=init(net);%%%训练样本%%%%[net,tr]=train(net,P,T);PIN0=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/4 (2).png'));PIN1=pretreatment(imread('Z:/data/PictureData/TestCode/SplitDataTest/3 (2).png'));P0=[PIN0',PIN1'];T0= sim(net ,PIN1')T1 = compet (T0) d =find(T1 == 1) - 1 fprintf('预测数字是：%d/n',d);%有较高的识别率