function main()
N=10;               %城市數
A=1.5;              %系數A
D=1;                %系數D
u0=0.02;            %神經元函數斜率
Step_t=0.1;         %計算步長
MaxEpochs=2000;     %迭代次數

%得到城市間距離矩陣
CityCood=rand(2,N);                              %城市坐標
DistanceMat=dist(CityCood',CityCood);             %城市間距離矩陣

U=0.2*rand(N,N)-0.1;                             %神經元輸入初始值在0附近隨機產生
for Count =1:MaxEpochs
    V=(1+tansig(U/u0))/2;
    E=CacuEnergy(V,DistanceMat,A,D)              %計算能量
    
    delta_U=CacuDeltaU(V,DistanceMat,A,D,Step_t);%計算U的增量
    U=U+delta_U*Step_t;
end
[NewV,CheckRes]=RouteCheck(V);                   %檢查V是否是有效路徑
if(CheckRes<1)
    FinalE=CacuEnergy(NewV,DistanceMat,A,D)
    RouteLen=TotalRouteLength(NewV,CityCood)     %計算路徑的真實長度
    PlotRoute(NewV,CityCood);                    %繪制路徑
else
    disp('路徑無效！');
end
%能量計算
function E=CacuEnergy(V,d,A,D)
[n,n]=size(V);
t1=sumsqr(sum(V,2)-1);
t2=sumsqr(sum(V,1)-1);
PermitV=V(:,2:n);
PermitV=[PermitV V(:,1)];
temp=d*PermitV;
t3=sum(sum(V.*temp))
E=0.5*(A*t1+A*t2+D*t3);

%計算U的增量
function d_U=CacuDeltaU(V,d,A,D,dt)
[n,n]=size(V);
t1=repmat(sum(V,2)-1,1,n);
t2=repmat(sum(V,1)-1,n,1);
PermitV=V(:,2:n);
PermitV=[PermitV V(:,1)];
t3=d*PermitV;
d_U=-dt*(A*t1+A*t2+D*t3);

%稽查V是否是有效路徑
function [NewV,CheckRes]=RouteCheck(V)
[rows,columns]=size(V);
NewV=zeros(rows,columns);
[XC,Order]=max(V);
for j=1:columns,
    NewV(Order(j),j)=1;
end
SC=sum(NewV);
SR=sum(NewV);
CheckRes=sumsqr(SC-SR);

%繪制路徑
function PlotRoute(V,CityCood)
figure;
title('TSP solution');
xlabel('X 坐標');
ylabel('Y 坐標');
axis([0,1,0,1])
axis on
[xxx,order]=max(V);
NewCood=CityCood(:,order);
NewCood=[NewCood NewCood(:,1)];
plot(NewCood(1,:),NewCood(2,:),'O-');

%計算路徑實際長度
function Len=TotalRouteLength(V,CityCood)
[xxx,order]=max(V);
NewCood=CityCood(:,order);
NewCood=[NewCood NewCood(:,1)];
[rows,columns]=size(NewCood);
Len=0;
for i=2:columns,
    Len=Len+dist(NewCood(:,i-1)',NewCood(:,i));
end