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MATLAB神经网络图像识别高识别率代码
I0=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\0 (1).png-600')); I1=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\1 (1).png-600')); I2=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\2 (1).png-600')); I3=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\3 (1).png-600')); I4=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\4 (1).png-600')); I5=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\5 (1).png-600')); I6=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\6 (1).png-600')); I7=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\7 (1).png-600')); I8=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\8 (1).png-600')); I9=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\9 (1).png-600')); %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-600')); PIN1=pretreatment(imread('Z:\data\PictureData\TestCode\SplitDataTest\3 (2).png-600')); 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 识别率还是挺高的。但是最大的难点问题是图像的预处理，分割，我觉得智能算法的识别已经做得很好了。最重要的是图像预处理分割。
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