粒子群训练bp神经网络的matlab程序

AllSamIn=[0.199 0.104 0.110 0.128 0.190 0.201 0.233 0.201 0.195 0.226 0.139 0.109 0.191 0.273 0.234;
          0.273 0.312 0.334 0.357 0.417 0.559 0.277 0.382 0.637 0.375 0.320 0.422 0.324 0.421 0.311;
          0.256 0.156 0.175 0.235 0.293 0.612 0.175 0.273 0.663 0.272 0.265 0.421 0.398 0.425 0.283;
          0.245 0.187 0.169 0.291 0.310 0.537 0.193 0.270 0.629 0.285 0.230 0.421 0.402 0.450 0.275;
          0.312 0.312 0.305 0.343 0.273 0.576 0.479 0.572 0.671 0.376 0.283 0.373 0.375 0.358 0.163;
          0.312 0.285 0.273 0.302 0.320 0.537 0.456 0.473 0.634 0.320 0.320 0.433 0.376 0.350 0.205;
          0.286 0.273 0.522 0.504 0.520 0.579 0.503 0.427 0.603 0.420 0.316 0.372 0.409 0.415 0.315;
          0.286 0.256 0.376 0.482 0.518 0.638 0.561 0.531 0.638 0.331 0.473 0.405 0.373 0.452 0.265;
         0.156 0.156 0.143 0.177 0.380 0.453 0.237 0.370 0.602 0.265 0.245 0.323 0.208 0.342 0.165;
         0.320 0.149 0.252 0.253 0.316 0.245 0.487 0.371 0.255 0.281 0.207 0.302 0.424 0.428 0.221;
         0.356 0.317 0.385 0.524 0.317 0.619 0.393 0.343 0.649 0.400 0.367 0.333 0.433 0.508 0.302;
         0.217 0.245 0.278 0.182 0.230 0.598 0.187 0.212 0.611 0.258 0.367 0.342 0.276 0.326 0.210;
         0.217 0.156 0.143 0.323 0.316 0.637 0.227 0.187 0.637 0.265 0.241 0.322 0.208 0.275 0.140;
         0.212 0.312 0.324 0.430 0.317 0.621 0.340 0.382 0.629 0.383 0.316 0.340 0.292 0.326 0.335;
         0.212 0.226 0.175 0.235 0.363 0.538 0.350 0.376 0.637 0.265 0.337 0.340 0.212 0.250 0.143]';%训练样本输入

AllSamOut=[1 0 0 0 0; 0 1 0 0 0;1 0 0 0 0;0 1 0 0 0;0 1 0 0 0;0 1 0 0 0;0 0  1 0 0;0 0 1 0 0;
     1 0 0 0 0;0 1 0 0 0;0 0 0 1 0;1 0 0 0 0;1 0 0 0 0;0 1 0 0 0;0 1 0 0 0]';   %训练样本输出

i=[5:5:15];       %取样本的20%作为测试样本
TestSamIn=[0.312 0.312 0.305 0.343 0.273 0.576 0.479 0.572 0.671 0.376 0.283 0.373 0.375 0.358 0.163;
   0.320 0.149 0.252 0.253 0.316 0.245 0.487 0.371 0.255 0.281 0.207 0.302 0.424 0.428 0.221;
   0.212 0.226 0.175 0.235 0.363 0.538 0.350 0.376 0.637 0.265 0.337 0.340 0.212 0.250 0.143]';      %测试样本输入

TestSamOut=[0 1 0 0 0;0 1 0 0 0;0 1 0 0 0]';     %测试样本对应输出

%添加估计样本
EvaSamIn=[0.172 0.156 0.163 0.227 0.317 0.537 0.235 0.315 0.512 0.275 0.265 0.341 0.212 0.346 0.165 ]';

  %TargetOfTestSam=[0 1 0 0 0;0 1 0 0 0;0 1 0 0 0]';      %添加目标测试样本

%**************************************
%样本、测试样本、估测样本归一化

global minAllSamOut;
global maxAllSamOut;%建立全局变量
%[AllSamInn,minAllSamIn,maxAllSamIn,AllSamOutn,minAllSamOut,maxAllSamOut] = premnmx(AllSamIn,AllSamOut);
%[TestSamInn,minTestSamIn,maxTestSamIn,RealTestSamOutn,minRealTestSamOut,maxRealTestSamOut] = premnmx(TestSamIn,RealTestSamOut);
%[AllSamInn,minAllSamIn,maxAllSamIn,AllSamOutn,minAllSamOut,maxAllSamOut] = mapminmax(AllSamIn,AllSamOut);%归一化
%[TestSamInn,minTestSamIn,maxTestSamIn,RealTestSamOutn,minRealTestSamOut,maxRealTestSamOut] = premnmx(TestSamIn,TestSamOut);
%EvaSamInn=tramnmx(EvaSamIn,minAllSamIn,maxAllSamIn); % preprocessing

for j=1:3
    TestSamIn=[TestSamIn,AllSamIn(:,i(j))]; 
    TestSamOut=[TestSamOut,AllSamOut(:,i(j))];
end
TrainSamIn=AllSamIn;
TrainSamOut=AllSamOut;
TrainSamIn(:,i)=[];
TrainSamOut(:,i)=[];
%[TrainSamInn,minTrainSamIn,maxTrainSamIn,TrainSamOutn,minTrainSamOut,maxTrainSamOut] = premnmx(TrainSamIn,TrainSamOut);%训练样本归一化

global Ptrain;
Ptrain = TrainSamIn;
global Ttrain;
Ttrain = TrainSamOut;
Ptest = TestSamIn;
Ttest = TestSamOut;
%****************************************************
%设置神经网络参数