给个BP神经网络的全代码给你吧
library(nnet)
source <- c(10930,10318,10595,10972,7706,6756,9092,10551,9722,10913,11151,8186,6422,
6337,11649,11652,10310,12043,7937,6476,9662,9570,9981,9331,9449,6773,6304,9355,10477,
10148,10395,11261,8713,7299,10424,10795,11069,11602,11427,9095,7707,10767,12136,12812,
12006,12528,10329,7818,11719,11683,12603,11495,13670,11337,10232,13261,13230,15535,
16837,19598,14823,11622,19391,18177,19994,14723,15694,13248,9543,12872,13101,15053,
12619,13749,10228,9725,14729,12518,14564,15085,14722,11999,9390,13481,14795,15845,
15271,14686,11054,10395,14775,14618,16029,15231,14246,12095,10473,15323,15381,14947)
srcLen<-length(source)
for(i in 1:10){ #预测最后十个数；
real <- source[srcLen-i+1] #实际值
xNum=(srcLen-i+1)%/%7 #组数
yNum=7 #每组7个数
data<-array(1:(xNum*yNum),c(xNum,yNum))
pre=srcLen-i+1;
for(x in 1:xNum){ #数组赋值
for(y in 1:yNum){
data[x,y]=source[pre]
pre=pre-1;
}
if(pre<7){
break;
}
}
ascData<-array(1:(xNum*yNum),c(xNum,yNum)) #数组逆序
for(x in 1:xNum){
for(y in 1:yNum){
ascData[x,y]=data[xNum-x+1,y]
}
}
colnames(ascData) <- c("a","b","c","d","e","f","g") #每列列名
trainData<-data.frame(scale(ascData[,c(1:7)]))
nn<-nnet(a~b+c+d+e+f+g,trainData[1:(xNum-1),],size=10,decay=0.01,maxit=1000,linout=F,trace=F)
predict<-predict(nn,trainData[xNum,])
predict=predict*sd(ascData[,1])+mean(ascData[,1])
percent <- (predict-real)*100/real
res <- paste("预测值：",predict,"实际值：",real,"误差：",percent)
print(res)
}
注意上面的测试数据有周期性...
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