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c语言指数函数程序递归,写一个程序,以尽量减少递归指数函数

递归实现指数函数c语言

此的平方之和是,我想中的R的代码的功能,写一个程序,以尽量减少递归指数函数

dMK9x.png

i = 1,2,3,....j-1

A,B,C,F,克将被从NLS确定(与起始值任意设定为7,30,15,1,2)

S且Y是在数据集

功能可以在一个更大的计算量被呈现人友好的递推方程,

050Vy.png

这里是我的代码的尝试,但我无法得到它的收敛,

S=c(235,90,1775,960,965,1110,370,485,667,140,588,10,0,1340,600,0,930,1250,930,120,895,825,0,935,695,270,0,610,0,0,445,0,0,370,470,819,717,0,0,60,0,135,690,0,825,730,1250,370,1010,261,0,865,570,1425,150,1515,1143,0,675,1465,375,0,690,290,0,430,735,510,270,450,1044,0,928,60,95,105,60,950,0,1640,3960,1510,500,1135,0,0,0,181,568,60,1575,247,0,1270,870,290,510,0,540,455,120,580,420,90,525,1116,499,0,60,150,660,1080,1715,90,1090,840,975,280,850,633,30,1530,1765,880,150,225,77,1380,810,835,0,540,1017,1108,0,300,600,90,370,910,0,60,60,0,0,0,0,50,0,735,900)

Y=c(NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,7.7,NA,NA,7.2,NA,NA,NA,NA,NA,NA,7.4,NA,NA,NA,NA,NA,NA,10.7,NA,NA,NA,NA,8.1,8.5,NA,NA,NA,NA,NA,9.9,NA,7.4,NA,NA,NA,9.5,NA,NA,9,NA,NA,NA,8.8,NA,NA,8.5,NA,NA,NA,6.9,NA,NA,7.9,NA,NA,NA,7.3,NA,7.9,8.3,NA,NA,NA,11.5,NA,NA,12.3,NA,NA,NA,6.1,NA,NA,9,NA,NA,NA,10.3,NA,NA,9.7,NA,NA,8.6,NA,9.1,NA,NA,11,NA,NA,12.4,11.1,10.1,NA,NA,NA,NA,11.7,NA,NA,9,NA,NA,NA,10.2,NA,NA,11.2,NA,NA,NA,11.8,NA,9.2,10,9.8,NA,9.5,11.3,10.3,9.5,10.2,10.6,NA,10.8,10.7,11.1,NA,NA,NA,NA,NA,NA,NA,NA,12.6,NA)

mydata = data.frame(Y,S)

f

model

model[1,1]=0

model[1,2]=0

model[1,3]=a

for (i in 2:nrow(model)){

model[i,1]=exp(-1/c)*model[i-1,1] + m$S[i-1]

model[i,2]=exp(-1/g)*model[i-1,2] + m$S[i-1]

model[i,3]=a+b*model[i,1]-f*model[i,2]

}

model

colnames(model) = c('l','m','Y')

model$Y[which(m$Y>0)]

}

Y=mydata$Y

nls(Y ~ f(a,b,f,c,g,mydata), start=list(a=7,b=5.3651,f=5.3656,c=16.50329,g=16.5006),control=list(maxiter=1000,minFactor=1e-12))

的错误,我已经得到依赖于初始值:

错误NLS(Y〜F(A,b,F,C,G,MYDATA),开始=列表(= 7,:

迭代次数超过10最大00

错误NLS(Y〜F(A,B,F,C,G,MYDATA),开始=列表(= 7,:

奇异梯度

我卡和不知道该怎么做,任何帮助将不胜感激。

2015-12-17

mct

+0

a)您必须构造MYDATA为data.frame使用$:'MYDATA < - data.frame(Y,S)'。 b)什么是'p0'? c)你可以运行'f(7,5,16,16,mydata)'吗?你会得到正确的结果吗?如果不是,nls不会工作... –

+0

对不起,p0 = a,更正了更正。 f(7,5,16,16,mydata)有效,它会生成预测的Y –

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