基于神经网络的马尾松毛虫精细化预报Matlab建模试验

基于神经网络的马尾松毛虫精细化预报Matlab建模试验

张国庆

（安徽省潜山县林业局）

1.数据来源

马尾松毛虫发生量、发生期数据来源于潜山县监测数据，气象数据来源于国家气候中心。

2.数据预处理

为了体现马尾松毛虫发生发展时间上的完整性，在数据处理时，将越冬代数据与上一年第二代数据合并，这样，就在时间上保持了一个马尾松毛虫世代的完整性，更便于建模和预测。

（1）气象数据处理

根据《松毛虫综合管理》、《中国松毛虫》等学术资料以及近年来有关马尾松毛虫监测预报学术论文，初步选择与松毛虫发生量、发生期有一定相关性气象因子，包括卵期极低气温，卵期平均气温，卵期积温（日度），卵期降雨量，第1、2龄极低气温，第1、2龄平均气温，第1、2龄积温（日度），第12龄降雨量，幼虫期极低气温，幼虫期平均气温，幼虫期积温（日度），幼虫期降雨量，世代极低气温，世代平均气温，世代积温（日度），世代降雨量共16个变量。将来自于国家气候中心的气象原始数据，按年度分世代转换成上述16个变量数据系列。

（2）发生量数据处理

为了在建模时分析发生强度，在对潜山县1983～2014年原始监测数据预处理时，按照“轻”、“中”、“重”3个强度等级，分类按世代逐年汇总。

（3）发生期数据处理

首先对潜山县1983～2014年原始发生期监测数据按世代逐年汇总，然后日期数据转换成日历天，使之数量化，以便于建模分析。

3.因子变量选择

通过相关性分析和建模试验比较，第一代发生量因子变量选择第1、2龄极低气温，卵期极低气温，上一代防治效果，上一代防治面积；第二代发生量因子变量选择第1、2龄极低气温，卵期极低气温，上一代防治效果，上一代防治面积，第1、2龄降雨量，卵期降雨量；第一代幼虫高峰期因子变量选择第1、2龄平均气温，第1、2龄积温（日度），第1、2龄极低气温，卵期极低气温；第二代幼虫高峰期因子变量选择成虫始见期，卵期平均气温，卵期积温（日度），第1、2龄极低气温。

将第一代发生量变量命名为s1y，因变量命名为s1x；第二代发生量变量命名为s2y，因变量命名为s2x；第一代幼虫高峰期变量命名为t1y，因变量命名为t1x；第二代幼虫高峰期变量命名为t2y，因变量命名为t2x。

4.第一代发生量建模试验

4.1程序代码

程序代码（SimpleScript）为：

%SolveanInput-OutputFittingproblemwithaNeuralNetwork

%ScriptgeneratedbyNeuralFittingapp

%CreatedWedOct2819:28:48CST2015

%

%Thisscriptassumesthesevariablesaredefined:

%

%s1x-inputdata.

%s1y-targetdata.

x=s1x';

t=s1y';

%ChooseaTrainingFunction

%Foralistofalltrainingfunctionstype:helpnntrain

%'trainlm'isusuallyfastest.

%'trainbr'takeslongerbutmaybebetterforchallengingproblems.

%'trainscg'useslessmemory.NFTOOLfallsbacktothisinlowmemorysituations.

trainFcn='trainlm';%Levenberg-Marquardt

%CreateaFittingNetwork

hiddenLayerSize=10;

net=fitnet(hiddenLayerSize,trainFcn);

%SetupDivisionofDataforTraining,Validation,Testing

net.divideParam.trainRatio=90/100;

net.divideParam.valRatio=5/100;

net.divideParam.testRatio=5/100;

%TraintheNetwork

[net,tr]=train(net,x,t);

%TesttheNetwork

y=net(x);

e=gsubtract(t,y);

performance=perform(net,t,y)

%ViewtheNetwork

view(net)

%Plots

%Uncommenttheselinestoenablevariousplots.

%figure,plotperform(tr)

%figure,plottrainstate(tr)

%figure,plotfit(net,x,t)

%figure,plotregression(t,y)

%figure,ploterrhist(e)

程序代码（AdvancedScript）为：

%SolveanInput-OutputFittingproblemwithaNeuralNetwork

%ScriptgeneratedbyNeuralFittingapp

%CreatedWedOct2819:29:03CST2015

%

%Thisscriptassumesthesevariablesaredefined:

%

%s1x-inputdata.

%s1y-targetdata.

x=s1x';

t=s1y';

%ChooseaTrainingFunction

%Foralistofalltrainingfunctionstype:helpnntrain

%'trainlm'isusuallyfastest.

%'trainbr'takeslongerbutmaybebetterforchallengingproblems.

%'trainscg'useslessmemory.NFTOOLfallsbacktothisinlowmemorysituations.

trainFcn='trainlm';%Levenberg-Marquardt

%CreateaFittingNetwork

hiddenLayerSize=10;

net=fitnet(hiddenLayerSize,trainFcn);

%ChooseInputandOutputPre/Post-ProcessingFunctions

%Foralistofallprocessingfunctionstype:helpnnprocess

cessFcns={'removeconstantrows','mapminmax'};

cessFcns={'removeconstantrows','mapminmax'};

%SetupDivisionofDataforTraining,Validation,Testing

%Foralistofalldatadivisionfunctionstype:helpnndivide

net.divideFcn='dividerand';%Dividedatarandomly

net.divideMode='sample';%Divideupeverysample

net.divideParam.trainRatio=90/100;

net.divideParam.valRatio=5/100;

net.divideParam.testRatio=5/100;

%ChooseaPerformanceFunction

%Foralistofallperformancefunctionstype:helpnnperformance

net.performFcn='mse';%Meansquarederror

%ChoosePlotFunctions

%Foralistofallplotfunctionstype:helpnnplot

net.plotFcns={'plotperform','plottrainstate','ploterrhist',...

'plotregression','plotfit'};

%TraintheNetwork

[net,tr]=train(net,x,t);

%TesttheNetwork

y=net(x);

e=gsubtract(t,y);

performance=perform(net,t,y)

%RecalculateTraining,ValidationandTestPerformance

trainTargets=t.*tr.trainMask{1};

valTargets=t.*tr.valMask{1};

testTargets=t.*tr.testMask{1};

trainPerformance=perform(net,trainTargets,y)

valPerformance=perform(net,valTargets,y)

testPerformance=perform(net,testTargets,y)

%ViewtheNetwork

view(net)

%Plots

%Uncommenttheselinestoenablevariousplots.

%figure,plotperform(tr)

%figure,plottrainstate(tr)

%figure,plotfit(net,x,t)

%figure,plotregression(t,y)

%figure,ploterrhist(e)

%Deployment

%Changethe(false)valuesto(true)toenablethefollowingcodeblocks.

if(false)

%GenerateMATLABfunctionforneuralnetworkforapplicationdeployment

%inMATLABscriptsorwithMATLABCompilerandBuildertools,orsimply

%toexaminethecalculationsyourtrainedneuralnetworkperforms.

genFunction(net,'myNeuralNetworkFunction');

y=myNeuralNetworkFunction(x);

end

if(false)

%Generateamatrix-onlyMATLABfunctionforneuralnetworkcode

%generationwithMATLABCodertools.

genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');

y=myNeuralNetworkFunction(x);

end

if(false)

%GenerateaSimulinkdiagramforsimulationordeploymentwith.

%SimulinkCodertools.

gensim(net);

end

4.2网络训练过程

网络训练为：

图1第一代发生量网络训练过程

4.3训练结果

训练结果为：

图2第一代发生量网络训练结果

训练样本、验证样本、测试样本的R值分别为0.875337、-1和1。

误差直方图为：

图3第一代发生量网络训练结果误差直方图

训练样本、验证样本、测试样本、所有数据回归图为：

图4第一代发生量网络训练结果回归图

验证样本和测试样本R值均为1。

5.第二代发生量建模试验

5.1程序代码

程序代码（SimpleScript）为：

%SolveanInput-OutputFittingproblemwithaNeuralNetwork

%ScriptgeneratedbyNeuralFittingapp

%CreatedWedOct2820:04:18CST2015

%

%Thisscriptassumesthesevariablesaredefined:

%

%s2x-inputdata.

%s2y-targetdata.

x=s2x';

t=s2y';

%ChooseaTrainingFunction

%Foralistofalltrainingfunctionstype:helpnntrain

%'trainlm'isusuallyfastest.

%'trainbr'takeslongerbutmaybebetterforchallengingproblems.

%'trainscg'useslessmemory.NFTOOLfallsbacktothisinlowmemorysituations.

trainFcn='trainlm';%Levenberg-Marquardt

%CreateaFittingNetwork

hiddenLayerSize=10;

net=fitnet(hiddenLayerSize,trainFcn);

%SetupDivisionofDataforTraining,Validation,Testing

net.divideParam.trainRatio=90/100;

net.divideParam.valRatio=5/100;

net.divideParam.testRatio=5/100;

%TraintheNetwork

[net,tr]=train(net,x,t);

%TesttheNetwork

y=net(x);

e=gsubtract(t,y);

performance=perform(net,t,y)

%ViewtheNetwork

view(net)

%Plots

%Uncommenttheselinestoenablevariousplots.

%figure,plotperform(tr)

%figure,plottrainstate(tr)

%figure,plotfit(net,x,t)

%figure,plotregression(t,y)

%figure,ploterrhist(e)

程序代码（AdvancedScript）为：

%SolveanInput-OutputFittingproblemwithaNeuralNetwork

%ScriptgeneratedbyNeuralFittingapp

%CreatedWedOct2820:04:31CST2015

%

%Thisscriptassumesthesevariablesaredefined:

%

%s2x-inputdata.

%s2y-targetdata.

x=s2x';

t=s2y';

%ChooseaTrainingFunction

%Foralistofalltrainingfunctionstype:helpnntrain

%'trainlm'isusuallyfastest.

%'trainbr'takeslongerbutmaybebetterforchallengingproblems.

%'trainscg'useslessmemory.NFTOOLfallsbacktothisinlowmemorysituations.

trainFcn='trainlm';%Levenberg-Marquardt

%CreateaFittingNetwork

hiddenLayerSize=10;

net=fitnet(hiddenLayerSize,trainFcn);

%ChooseInputandOutputPre/Post-ProcessingFunctions

%Foralistofallprocessingfunctionstype:helpnnprocess

cessFcns={'removeconstantrows','mapminmax'};

cessFcns={'removeconstantrows','mapminmax'};

%SetupDivisionofDataforTraining,Validation,Testing

%Foralistofalldatadivisionfunctionstype:helpnndivide

net.divideFcn='dividerand';%Dividedatarandomly

net.divideMode='sample';%Divideupeverysample

net.divideParam.trainRatio=90/100;

net.divideParam.valRatio=5/100;

net.divideParam.testRatio=5/100;

%ChooseaPerformanceFunction

%Foralistofallperformancefunctionstype:helpnnperformance

net.performFcn='mse';%Meansquarederror

%ChoosePlotFunctions

%Foralistofallplotfunctionstype:helpnnplot

net.plotFcns={'plotperform','plottrainstate','ploterrhist',...

'plotregression','plotfit'};

%TraintheNetwork

[net,tr]=train(net,x,t);

%TesttheNetwork

y=net(x);

e=gsubtract(t,y);

performance=perform(net,t,y)

%RecalculateTraining,ValidationandTestPerformance

trainTargets=t.*tr.trainMask{1};

valTargets=t.*tr.valMask{1};

testTargets=t.*tr.testMask{1};

trainPerformance=perform(net,trainTargets,y)

valPerformance=perform(net,valTargets,y)

testPerformance=perform(net,testTargets,y)

%ViewtheNetwork

view(net)

%Plots

%Uncommenttheselinestoenablevariousplots.

%figure,plotperform(tr)

%figure,plottrainstate(tr)

%figure,plotfit(net,x,t)

%figure,plotregression(t,y)

%figure,ploterrhist(e)

%Deployment

%Changethe(false)valuesto(true)toenablethefollowingcodeblocks.

if(false)

%GenerateMATLABfunctionforneuralnetworkforapplicationdeployment

%inMATLABscriptsorwithMATLABCompilerandBuildertools,orsimply

%toexaminethecalculationsyourtrainedneuralnetworkperforms.

genFunction(net,'myNeuralNetworkFunction');

y=myNeuralNetworkFunction(x);

end

if(false)

%Generateamatrix-onlyMATLABfunctionforneuralnetworkcode

%generationwithMATLABCodertools.

genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');

y=myNeuralNetworkFunction(x);

end

if(false)

%GenerateaSimulinkdiagramforsimulationordeploymentwith.

%SimulinkCodertools.

gensim(net);

end

5.2网络训练过程

网络训练为：

图5第二代发生量网络训练过程

5.3训练结果

训练结果为：

图6第二代发生量网络训练结果

训练样本、验证样本、测试样本的R值分别为0.942388、0.999999和1。

误差直方图为：

图7第二代发生量网络训练结果误差直方图

训练样本、验证样本、测试样本、所有数据回归图为：

图8第二代发生量网络训练结果回归图

验证样本和测试样本R值均为1，训练样本R=0.94239，所有数据R=0.89479。

6.第一代幼虫高峰期建模试验

6.1程序代码

程序代码（SimpleScript）为：

%SolveanInput-OutputFittingproblemwithaNeuralNetwork

%ScriptgeneratedbyNeuralFittingapp

%CreatedWedOct2820:16:32CST2015

%

%Thisscriptassumesthesevariablesaredefined:

%

%t1x-inputdata.

%t1y-targetdata.

x=t1x';

t=t1y';

%ChooseaTrainingFunction

%Foralistofalltrainingfunctionstype:helpnntrain

%'trainlm'isusuallyfastest.

%'trainbr'takeslongerbutmaybebetterforchallengingproblems.

%'trainscg'useslessmemory.NFTOOLfallsbacktothisinlowmemorysituations.

trainFcn='trainlm';%Levenberg-Marquardt

%CreateaFittingNetwork

hiddenLayerSize=10;

net=fitnet(hiddenLayerSize,trainFcn);

%SetupDivisionofDataforTraining,Validation,Testing

net.divideParam.trainRatio=90/100;

net.divideParam.valRatio=5/100;

net.divideParam.testRatio=5/100;

%TraintheNetwork

[net,tr]=train(net,x,t);

%TesttheNetwork

y=net(x);

e=gsubtract(t,y);

performance=perform(net,t,y)

%ViewtheNetwork

view(net)

%Plots

%Uncommenttheselinestoenablevariousplots.

%figure,plotperform(tr)

%figure,plottrainstate(tr)

%figure,plotfit(net,x,t)

%figure,plotregression(t,y)

%figure,ploterrhist(e)

程序代码（AdvancedScript）为：

%SolveanInput-OutputFittingproblemwithaNeuralNetwork

%ScriptgeneratedbyNeuralFittingapp

%CreatedWedOct2820:17:08CST2015

%

%Thisscriptassumesthesevariablesaredefined:

%

%t1x-inputdata.

%t1y-targetdata.

x=t1x';

t=t1y';

%ChooseaTrainingFunction

%Foralistofalltrainingfunctionstype:helpnntrain

%'trainlm'isusuallyfastest.

%'trainbr'takeslongerbutmaybebetterforchallengingproblems.

%'trainscg'useslessmemory.NFTOOLfallsbacktothisinlowmemorysituations.

trainFcn='trainlm';%Levenberg-Marquardt

%CreateaFittingNetwork

hiddenLayerSize=10;

net=fitnet(hiddenLayerSize,trainFcn);

%ChooseInputandOutputPre/Post-ProcessingFunctions

%Foralistofallprocessingfunctionstype:helpnnprocess

cessFcns={'removeconstantrows','mapminmax'};

cessFcns={'removeconstantrows','mapminmax'};

%SetupDivisionofDataforTraining,Validation,Testing

%Foralistofalldatadivisionfunctionstype:helpnndivide

net.divideFcn='dividerand';%Dividedatarandomly

net.divideMode='sample';%Divideupeverysample

net.divideParam.trainRatio=90/100;

net.divideParam.valRatio=5/100;

net.divideParam.testRatio=5/100;

%ChooseaPerformanceFunction

%Foralistofallperformancefunctionstype:helpnnperformance

net.performFcn='mse';%Meansquarederror

%ChoosePlotFunctions

%Foralistofallplotfunctionstype:helpnnplot

net.plotFcns={'plotperform','plottrainstate','ploterrhist',...

'plotregression','plotfit'};

%TraintheNetwork

[net,tr]=train(net,x,t);

%TesttheNetwork

y=net(x);

e=gsubtract(t,y);

performance=perform(net,t,y)

%RecalculateTraining,ValidationandTestPerformance

trainTargets=t.*tr.trainMask{1};

valTargets=t.*tr.valMask{1};

testTargets=t.*tr.testMask{1};

trainPerformance=perform(net,trainTargets,y)

valPerformance=perform(net,valTargets,y)

testPerformance=perform(net,testTargets,y)

%ViewtheNetwork

view(net)

%Plots

%Uncommenttheselinestoenablevariousplots.

%figure,plotperform(tr)

%figure,plottrainstate(tr)

%figure,plotfit(net,x,t)

%figure,plotregression(t,y)

%figure,ploterrhist(e)

%Deployment

%Changethe(false)valuesto(true)toenablethefollowingcodeblocks.

if(false)

%GenerateMATLABfunctionforneuralnetworkforapplicationdeployment

%inMATLABscriptsorwithMATLABCompilerandBuildertools,orsimply

%toexaminethecalculationsyourtrainedneuralnetworkperforms.

genFunction(net,'myNeuralNetworkFunction');

y=myNeuralNetworkFunction(x);

end

if(false)

%Generateamatrix-onlyMATLABfunctionforneuralnetworkcode

%generationwithMATLABCodertools.

genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');

y=myNeuralNetworkFunction(x);

end

if(false)

%GenerateaSimulinkdiagramforsimulationordeploymentwith.

%SimulinkCodertools.

gensim(net);

end

6.2网络训练过程

网络训练为：

图9第一代幼虫高峰期网络训练过程

6.3训练结果

训练结果为：

图10第一代幼虫高峰期网络训练结果

训练样本、验证样本、测试样本的R值分别为0.875337、-1和1。

误差直方图为：

图11第一代幼虫高峰期网络训练结果误差直方图

训练样本、验证样本、测试样本、所有数据回归图为：

图12第一代幼虫高峰期网络训练结果回归图

验证样本和测试样本R值均为1。

7.第二代幼虫高峰期建模试验

7.1程序代码

程序代码（SimpleScript）为：

%SolveanInput-OutputFittingproblemwithaNeuralNetwork

%ScriptgeneratedbyNeuralFittingapp

%CreatedWedOct2820:22:04CST2015

%

%Thisscriptassumesthesevariablesaredefined:

%

%t2x-inputdata.

%t2y-targetdata.

x=t2x';

t=t2y';

%ChooseaTrainingFunction

%Foralistofalltrainingfunctionstype:helpnntrain

%'trainlm'isusuallyfastest.

%'trainbr'takeslongerbutmaybebetterforchallengingproblems.

%'trainscg'useslessmemory.NFTOOLfallsbacktothisinlowmemorysituations.

trainFcn='trainlm';%Levenberg-Marquardt

%CreateaFittingNetwork

hiddenLayerSize=10;

net=fitnet(hiddenLayerSize,trainFcn);

%SetupDivisionofDataforTraining,Validation,Testing

net.divideParam.trainRatio=90/100;

net.divideParam.valRatio=5/100;

net.divideParam.testRatio=5/100;

%TraintheNetwork

[net,tr]=train(net,x,t);

%TesttheNetwork

y=net(x);

e=gsubtract(t,y);

performance=perform(net,t,y)

%ViewtheNetwork

view(net)

%Plots

%Uncommenttheselinestoenablevariousplots.

%figure,plotperform(tr)

%figure,plottrainstate(tr)

%figure,plotfit(net,x,t)

%figure,plotregression(t,y)

%figure,ploterrhist(e)

程序代码（AdvancedScript）为：

%SolveanInput-OutputFittingproblemwithaNeuralNetwork

%ScriptgeneratedbyNeuralFittingapp

%CreatedWedOct2820:22:29CST2015

%

%Thisscriptassumesthesevariablesaredefined:

%

%t2x-inputdata.

%t2y-targetdata.

x=t2x';

t=t2y';

%ChooseaTrainingFunction

%Foralistofalltrainingfunctionstype:helpnntrain

%'trainlm'isusuallyfastest.

%'trainbr'takeslongerbutmaybebetterforchallengingproblems.

%'trainscg'useslessmemory.NFTOOLfallsbacktothisinlowmemorysituations.

trainFcn='trainlm';%Levenberg-Marquardt

%CreateaFittingNetwork

hiddenLayerSize=10;

net=fitnet(hiddenLayerSize,trainFcn);

%ChooseInputandOutputPre/Post-ProcessingFunctions

%Foralistofallprocessingfunctionstype:helpnnprocess

cessFcns={'removeconstantrows','mapminmax'};

cessFcns={'removeconstantrows','mapminmax'};

%SetupDivisionofDataforTraining,Validation,Testing

%Foralistofalldatadivisionfunctionstype:helpnndivide

net.divideFcn='dividerand';%Dividedatarandomly

net.divideMode='sample';%Divideupeverysample

net.divideParam.trainRatio=90/100;

net.divideParam.valRatio=5/100;

net.divideParam.testRatio=5/100;

%ChooseaPerformanceFunction

%Foralistofallperformancefunctionstype:helpnnperformance

net.performFcn='mse';%Meansquarederror

%ChoosePlotFunctions

%Foralistofallplotfunctionstype:helpnnplot

net.plotFcns={'plotperform','plottrainstate','ploterrhist',...

'plotregression','plotfit'};

%TraintheNetwork

[net,tr]=train(net,x,t);

%TesttheNetwork

y=net(x);

e=gsubtract(t,y);

performance=perform(net,t,y)

%RecalculateTraining,ValidationandTestPerformance

trainTargets=t.*tr.trainMask{1};

valTargets=t.*tr.valMask{1};

testTargets=t.*tr.testMask{1};

trainPerformance=perform(net,trainTargets,y)

valPerformance=perform(net,valTargets,y)

testPerformance=perform(net,testTargets,y)

%ViewtheNetwork

view(net)

%Plots

%Uncommenttheselinestoenablevariousplots.

%figure,plotperform(tr)

%figure,plottrainstate(tr)

%figure,plotfit(net,x,t)

%figure,plotregression(t,y)

%figure,ploterrhist(e)

%Deployment

%Changethe(false)valuesto(true)toenablethefollowingcodeblocks.

if(false)

%GenerateMATLABfunctionforneuralnetworkforapplicationdeployment

%inMATLABscriptsorwithMATLABCompilerandBuildertools,orsimply

%toexaminethecalculationsyourtrainedneuralnetworkperforms.

genFunction(net,'myNeuralNetworkFunction');

y=myNeuralNetworkFunction(x);

end

if(false)

%Generateamatrix-onlyMATLABfunctionforneuralnetworkcode

%generationwithMATLABCodertools.

genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');

y=myNeuralNetworkFunction(x);

end

if(false)

%GenerateaSimulinkdiagramforsimulationordeploymentwith.

%SimulinkCodertools.

gensim(net);

end

7.2网络训练过程

网络训练为：

图13第二代幼虫高峰期网络训练过程

7.3训练结果

训练结果为：

图14第二代幼虫高峰期网络训练结果

训练样本、验证样本、测试样本的R值分别为0.402150、1和1。

误差直方图为：

图15第二代幼虫高峰期网络训练结果误差直方图

训练样本、验证样本、测试样本、所有数据回归图为：

图16第二代幼虫高峰期网络训练结果回归图

验证样本和测试样本R值均为1。

8.原始数据

第一代发生量变量名为s1y，因变量名为s1x；第二代发生量变量名为s2y，因变量名为s2x；第一代幼虫高峰期变量名为t1y，因变量名为t1x；第二代幼虫高峰期变量名为t2y，因变量名为t2x。原始数据为：

s1x=

1.0e+04*

0.00180.001800

0.00200.00200.00010.1054

0.00180.00180.00010.1651

0.00200.00200.00010.0667

0.00130.00130.00010.2980

0.00190.00210.00011.0873

0.00180.001900

0.00210.00210.00010.6967

0.00200.00190.00010.3207

0.00190.001900

0.00190.00170.00010.0100

0.00190.00200.00010.0337

0.00190.00190.00010.6313

0.00190.00170.00010.0380

0.00170.00170.00013.3381

0.00190.001900

0.00190.001900

0.00180.001800

0.00200.001900

0.00240.002400

0.00190.00190.00010.5500

0.00180.00180.00011.7041

0.00210.00210.00010.1785

0.00190.00190.00010.0607

0.00200.00200.00010.5295

0.00190.00200.00010.5240

0.00190.001900.0228

0.00180.001800.0333

0.00200.00200.00001.2040

0.00220.002000

0.00160.001600

0.00200.001700

s1y=

1.0e+04*

0.4639

0.8096

1.2294

0.7667

1.4733

0.2387

0.2042

0.1312

0.0100

0.0031

0.0030

0.2434

0.0764

0.5497

0.0362

0

0.0327

0.1533

0.0017

0.2313

0.4684

0.1733

0.1282

0.0183

0.0283

0.0367

0.0319

0.0720

0.3400

0.0687

0.2200

0.2200

s2x=

1.0e+03*

0.02120.36670.01970.00070.07660.0798

0.01791.31870.02010.00080.16950.1504

0.02191.81670.02190.00080.01450.0112

0.02162.04000.02180.00080.01800.0096

0.01998.26670.02380.00080.06080.0623

0.02090.08670.01940.00090.21560.1275

0.01861.22800.02090.00080.11990.1103

0.02091.11290.02090.00070.01340.0044

0.019200.022700.07170.0007

0.01730.03130.02180.00090.03970.0137

0.017600.017600.01780.0178

0.02100.80000.02380.00080.08670.0442

0.01990.30670.02200.00090.00110.0518

0.020900.020900.04260.0587

0.017900.020500.04780.0475

0.021300.022200.01180.0039

0.021000.020500.15340.3282

0.017800.022200.05070.0461

0.020900.020900.00330.0299

0.02132.31330.02510.00080.01540.0133

0.01972.88670.01970.00080.01160.0049

0.01951.73330.02170.00080.05780.0757

0.02001.28200.02110.00090.13160.1654

0.017600.024500.01540.0564

0.01900.56670.01900.00080.10270.1208

0.02001.76670.021000.05280.0476

0.01780.40000.017800.02430.0243

0.02100.20000.021000.13430.1161

0.020200.019100.02160.0806

0.020300.020700.10320.0089

0.017500.020300.09940.0799

0.018900.021300.02820.0344

s2y=

1.0e+04*

0.9162

0.8164

1.0393

0.6327

1.7020

0.0989

0.6046

0.3207

0.0617

0.0102

0.0400

0.6903

0.2784

3.3381

0.0200

0

0.0327

0.1333

0.2517

1.0533

1.8514

0.1785

0.0607

0.0367

0.0387

0.0215

0.0333

3.4874

0.2610

0.1933

0.2333

0.2000

t1x=

23.9000358.600018.300017.9000

25.3000378.800019.700019.7000

25.0000375.300017.500017.5000

24.4000366.700019.800019.8000

22.5000336.800013.200013.2000

26.3000395.200018.700020.9000

25.3000379.500018.100019.4000

26.6000399.500021.000020.6000

25.0000374.400019.900018.5000

25.5000381.800018.600018.6000

26.9000404.000019.300016.7000

23.9000357.800019.100019.7000

23.9000359.000019.000019.0000

26.5000398.100018.700016.9000

25.1000375.900017.300017.3000

24.7000370.300018.900018.9000

24.5000368.000018.700018.7000

25.4000381.400018.300018.3000

25.5000383.000019.900018.5000

28.6000429.500023.700023.7000

26.2000393.500019.500019.5000

24.4000365.400018.300018.3000

27.2000407.300021.100021.1000

27.8000416.500018.500018.5000

25.7000386.100019.700019.7000

24.6000368.300019.200019.6000

27.1000406.900018.800018.8000

24.5000367.400018.100018.1000

24.3000364.100020.100020.1000

27.0000405.300022.000020.1000

25.5000381.900016.300016.3000

26.4000396.000020.400017.0000

t1y=

162

163

162

162

162

163

160

160

160

160

159

158

158

159

158

158

158

157

156

156

156

157

156

156

155

156

155

155

155

156

155

155

t2x=

137.000026.9000349.200021.2000

137.000027.3000355.100017.9000

136.000028.1000364.900021.9000

133.000026.4000343.300021.6000

134.000028.7000373.700019.9000

133.000024.0000312.300020.9000

131.000026.0000338.200018.6000

130.000028.2000366.500020.9000

130.000028.9000375.200019.2000

130.000026.5000344.900017.3000

128.000025.3000329.100017.6000

128.000029.0000376.400021.0000

126.000029.3000380.400019.9000

126.000026.1000339.000020.9000

123.000027.4000356.500017.9000

122.000027.8000361.900021.3000

120.000024.4000316.900021.0000

120.000027.5000357.500017.8000

118.000025.6000332.900020.9000

117.000029.6000385.200021.3000

117.000029.3000380.900019.7000

118.000026.4000343.200019.5000

117.000024.8000322.500020.0000

117.000030.3000393.400017.6000

117.000027.6000359.100019.0000

118.000026.4000342.600020.0000

117.000027.5000357.400017.8000

117.000026.6000346.100021.0000

117.000025.2000327.900020.2000

118.000026.6000345.700020.3000

117.000027.3000354.500017.5000

117.000025.0000324.800018.9000

t2y=

253

254

253

253

253

254

252

252

252

253

250

249

249

250

249

249

249

249

248

248

248

249

248

248

248

249

248

248

248

249

248

248

附录资料：不需要的可以自行删除

Pascal/C/C++语句对比（补充版）

一、Helloworld

先看三种语言的样例：

Pascal

begin

writeln(‘Helloworld’);

end.

C

#include<stdio.h>

intmain()

{

printf("Helloworld!\n");

return0;

}

C++

#include<iostream>

usingnamespacestd;

intmain()

{

cout<<"Helloworld!"<<endl;

return0;

}

从这三个程序可以看到一些最基本的东西。在Pascal中的begin和end，在C/C++里就是{}；Pascal主程序没有返回值，而C/C++返回0（好像在C中可以为NULL）。在C/C++中，main函数以前的是头文件，样例中C为stdio.h，C++除了iostream还有第二行的usingnamespacestd，这个是打开命名空间的，NOIP不会考这个，可以不管，只要知道就行了。

此外说明注释单行用//，段落的话Pascal为{}，C/C++为/**/。

**常用头文件（模板）

#include<iostream>

#include<cstdio>

#include<cstdlib>

#include<cmath>

#include<ctime>

#include<string>

usingnamespacestd;

intmain()

{

……

system(“pause”);

return0;

}

二、数据类型及定义

这里只列出常用的类型。

1、整型

Pascal

C/C++

范围

shortint

-

-128…127

integer

short

-32768…32767

longint

Int

-2147483648…2147483647

int64

longlong

-9223372036854775808…9223372036854775807

byte

-

0…255

word

unsignedshort

0…65535

longword

unsignedint

0…4294967295

qword

unsign