matlab神经网络图片识别,基于MATLAB神经网络图像识别的高识别率代码

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'));

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

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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