MATLAB神經(jīng)網(wǎng)絡圖像識別高識別率代碼

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'));

%以上數(shù)據(jù)都是歸一化好的數(shù)據(jù)。

P=[I0',I1',I2',I3',I4',I5',I6',I7',I8',I9'];

T=eye(10,10);

%%bp神經(jīng)網(wǎng)絡參數(shù)設置

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);

%%%訓練樣本%%%%

[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('預測數(shù)字是：%d\n',d);

%有較高的識別率

識別率還是挺高的。但是最大的難點問題是圖像的預處理，分割，我覺得智能算法的識別已經(jīng)做得很好了。最重要的是圖像預處理分割。

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