111

%读取训练数据
[C1,C2,C3,C4,C5,C6,C7,C8,C9,C10,C11,C12,C13,C14,C15,C16,C17,C17,C19,C20,C21,C22,C23,C24,C25,C26,C27,C28,C29,C30,C31,C32,exp] = textread(‘C:\Users\geek\Desktop\data\testdata.txt‘ , ‘%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f‘,50);
n=50;
for i=1 :n
    C15(i)=0;
    C27(i)=0;
    C28(i)=0;
end
%特征值归一化
[input,minI,maxI] = premnmx( [C1,C2,C3,C4,C5,C6,C7,C8,C9,C10,C11,C12,C13,C14,C15,C16,C17,C17,C19,C20,C21,C22,C23,C24,C25,C26,C27,C28,C29,C30,C31,C32 ]‘);

%构造输出矩阵
s = length(exp) ;
output = zeros( s , 3  ) ;
for i = 1 : s 
   output( i , exp( i)+2  ) = 1 ;
end

%创建神经网络
net = newff( minmax(input) , [5 1] , { ‘logsig‘ ‘purelin‘ } , ‘traingdx‘ );
%设置训练参数
net.trainparam.show = 50 ;
net.trainparam.epochs = 1000;
net.trainparam.goal = 0.001 ;
net.trainParam.lr = 0.001 ;

%开始训练
[net,tr,Y,E,Pf,Af] = train( net, input , exp‘ ) ;


%读取测试数据
[T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,T17,T17,T19,T20,T21,T22,T23,T24,T25,T26,T27,T28,T29,T30,T31,T32,ver] = textread(‘C:\Users\geek\Desktop\data\verdata.txt‘ , ‘%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f‘,7);


%测试数据归一化
testInput = tramnmx ( [T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16,T17,T17,T19,T20,T21,T22,T23,T24,T25,T26,T27,T28,T29,T30,T31,T32]‘ , minI, maxI ) ;

%仿真
Z = sim( net , testInput ) ;
Y
E
Z