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дµÄ³ÌÐòÔ¤²â½á¹û·´¹éÒ»»¯²»ÁË£¬»¹Çë¸÷λ´óÉñÖ¸½Ì°¡£¡£¡ clear all clc m_data=[47.2 241 175 0.47 0.42 0 70 59 40.9; 47.2 187 170 0.50 0.43 51 102 0 40.1; 47.2 187 165 0.49 0.43 51 102 0 36.2; 49.8 260 180 0.49 0.42 0 55 55 39.4; 49.8 242 180 0.49 0.42 19 55 55 39.6; 49.8 242 180 0.49 0.42 19 55 55 37.0; 47.2 260 180 0.49 0.42 0 55 55 31.6; 47.2 207 162 0.47 0.47 52 86 0 41.8; 47.2 366 165 0.29 0.38 25 89 80 62.7; 47.2 372 159 0.30 0.41 79 79 0 68.3; 47.2 364 160 0.31 0.40 78 78 0 64.9]; p1=m_data(:,1:8);%ȡֵ130ÐУ¬Ç°°ËÁÐ t1=m_data(:,9);%ȡֵ130ÐУ¬ºó1ÁÐ p=p1';t=t1';%תÖã¨Õâ¸öµØ·½±ØÐëҪתÖã© fun = @(x)(x-min(x( ))/(max(x( )-min(x( )); %¹éÒ»»¯º¯Êýy = fun(p) z = fun(t) y; z; %clc n=20; net=newff(y,z,[n],{'tansig','purelin'},'trainlm'); inputWeights=net.IW{1,1}; inputbias=net.b{1}; layerWeights=net.IW{1,1}; layerbias=net.b{2}; net.trainParam.show=50; net.trainParam.lr=0.005; net.trainParam.epochs=2000; net.trainParam.goal=0.0001; net=train(net,y,z); An=sim(net,y); O=An-z; P=sse(O) Q=mse(O) y_predict=An*(max(p(i, )-min(p(i, ))+min(p(i, );y_predict A=An*(max(p( )-min(p( ))+min(p( ) ÈçºÎ½«An·´¹éÒ»»¯E=A-t;%¼ÆËãÎó²î M=sse(E)%¼ÆËãºÍ·½²î N=mse(E)%¼ÆËã¾ù·½²î ·´¹éÒ»»¯×ÜÊÇ´íÎó £¬Ôõô°ì°¡ |
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