求反求參數(shù)（無約束非線性優(yōu)化方法）matlab實現(xiàn)

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1、1．求反求參數(shù)（無約束非線性優(yōu)化方法）的matlab實現(xiàn)求反求參數(shù)（無約束非線性優(yōu)化方法）的matlab實現(xiàn)參數(shù)k,d。初始值代入差分得到計算值T算，（由程序），給出實測值T測，目標函數(shù)各點計算值與實測值的差最小。？因參數(shù)和目標函數(shù)間通過一系列數(shù)值計算，不知有下面的方法能否得到k,d想用Nelder-Mead法（單純形法）優(yōu)化：%Nelder-Mead法解最優(yōu)化問題%-函數(shù)f(x1,x2,...,xn)是一個有N個實數(shù)變量的函數(shù)，而且給定N+1個初始點%Vk=(vk,1,...,vk,N),k=0,1,...,Nfunction[v0,y0,dv,dy]=nelder(F

2、,V,min1,max1,epsilon,show)%Input-Fistheobjectfunctioninputasstring'F'%??????-Visa3*nmatrixcontaioningstaringsimplex%??????-min1&max1areminimunandmaximunnumber%????????ofiterations%??????-epsilonisthetolerance%??????-show==1displaysiterations(pandQ)%output-v0isthevertexfortheminimun%??????

3、-y0isthefunctionvalueF(v0)%??????-dVisthesizeofthefinalsimplex%??????-dyistheerrorboundfortheminimum%??????-p??isamatrixcontainingthevertexiterations%??????-Q??isanarraycontainingtheiterationsforF(p)ifnargin==5,??show=0;end[mmn]=size(V);%Ordertheverticesforj=1:n+1??z=V(j,1:n);??Y(j)=feval

4、(F,z);end[mmlo]=min(Y);[mmhi]=max(Y);li=hi;ho=lo;forj=1:n+1??if(j~=lo&j~=hi&Y(j)<=Y(li))????li=j;endif(j~=hi&j~=lo&Y(j)>Y(li))??ho=j;??endendcnt=0;%Startofnelder-MeadalgorithmWhile(Y(hi)>Y(lo)+epsilon&cnt

5、cnt

6、;??R=2*M-V(hi,1:n);??yR=feval(F,R);??if(yR

7、n)=R;??????Y(hi)=yR;??????end??????c=(V(hi,1:n)+M)/2;??????yc=feval(F,c);??????c2(M+R)/2;??????yc2=feval(F,c2);??????if(yc2

8、(j,1:n)+V(lo,1:n))/2;????????????z=V(j,1:n);????????????Y(j)=feval(F,z);??????????end??????end????end??end??[mmlo]=min(Y);??[mmhi]=max(Y);??li=hi;??ho=lo;??forj=1;n+1????if(j~=lo&j~=hi&Y(j)<=Y(li))??????li=j;????end????if(j~=hi&j~=lo&Y(j)>Y(ho))??????ho=j;????end??e