MATLAB线性神经网络的程序，跪求。。

美国Michigan 大学的 Holland 教授提出的遗传算法(GeneticAlgorithm, GA)是求解复杂的组合优化问题的有效方法 ，其思想来自于达尔文进化论和门德尔松遗传学说 ，它模拟生物进化过程来从庞大的搜索空间渗旁中筛选出较优秀的解，是一种高效而且具有强鲁棒性方法。所以，遗传算法在求解TSP和 MTSP问题中得到了广泛的应用。

matlab程序如下：

function[opt_rte,opt_brk,min_dist] =mtspf_ga(xy,dmat,salesmen,min_tour,pop_size,num_iter)

%%

%实例

%     n = 20%城市个数

%     xy = 10*rand(n,2)%城市坐标  随机产生，也可以自己设定

%     salesmen = 5%旅行商个数

%     min_tour = 3%每个旅行商最少访问的城市数

%     pop_size = 80%种群个数

%     num_iter = 200%迭代次数

%     a = meshgrid(1:n)

%     dmat =reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),n,n)

%     [opt_rte,opt_brk,min_dist] = mtspf_ga(xy,dmat,salesmen,min_tour,...

%         pop_size,num_iter)%函数

%%

[N,dims]= size(xy)%城市矩阵大小

[nr,nc]= size(dmat)%城市距离矩阵大小

n = N -1% 除去起始的城市后剩余的城市的数

% 初始化路线、断点的选择

num_brks= salesmen-1

dof = n- min_tour*salesmen       %初丛仔橡始化路线、断点的选择

addto =ones(1,dof+1)

for k =2:num_brks

addto = cumsum(addto)

end

cum_prob= cumsum(addto)/sum(addto)

%% 初始化种群

pop_rte= zeros(pop_size,n)          %   种群路径

pop_brk= zeros(pop_size,num_brks)    % 断点集合的种群

for k =1:pop_size

pop_rte(k,:) = randperm(n)+1

pop_brk(k,:) = randbreaks()

end

%  画图路径曲线颜色

clr =[1 0 00 0 10.67 0 10 1 01 0.5 0]

ifsalesmen >戚镇 5

clr = hsv(salesmen)

end

%%

% 基于遗传算法的MTSP

global_min= Inf        %初始化最短路径

total_dist= zeros(1,pop_size)

dist_history= zeros(1,num_iter)

tmp_pop_rte= zeros(8,n)%当前的路径设置

tmp_pop_brk= zeros(8,num_brks)%当前的断点设置

new_pop_rte= zeros(pop_size,n)%更新的路径设置

new_pop_brk= zeros(pop_size,num_brks)%更新的断点设置

foriter = 1:num_iter

% 计算适应值

for p = 1:pop_size

d = 0

p_rte = pop_rte(p,:)

p_brk = pop_brk(p,:)

rng = [[1 p_brk+1][p_brk n]]'

for s = 1:salesmen

d = d + dmat(1,p_rte(rng(s,1)))% 添加开始的路径

for k = rng(s,1):rng(s,2)-1

d = d + dmat(p_rte(k),p_rte(k+1))

end

d = d + dmat(p_rte(rng(s,2)),1)% 添加结束的的路径

end

total_dist(p) = d

end

% 找到种群中最优路径

[min_dist,index] = min(total_dist)

dist_history(iter) = min_dist

if min_dist <global_min

global_min = min_dist

opt_rte = pop_rte(index,:)%最优的最短路径

opt_brk = pop_brk(index,:)%最优的断点设置

rng = [[1 opt_brk+1][opt_brk n]]'%设置记录断点的方法

figure(1)

for s = 1:salesmen

rte = [1 opt_rte(rng(s,1):rng(s,2))1]

plot(xy(rte,1),xy(rte,2),'.-','Color',clr(s,:))

title(sprintf('城市数目为 = %d,旅行商数目为 = %d,总路程 = %1.4f, 迭代次数 =%d',n+1,salesmen,min_dist,iter))

hold on

grid on

end

plot(xy(1,1),xy(1,2),'ko')

hold off

end

% 遗传 *** 作

rand_grouping = randperm(pop_size)

for p = 8:8:pop_size

rtes = pop_rte(rand_grouping(p-7:p),:)

brks = pop_brk(rand_grouping(p-7:p),:)

dists =total_dist(rand_grouping(p-7:p))

[ignore,idx] = min(dists)

best_of_8_rte = rtes(idx,:)

best_of_8_brk = brks(idx,:)

rte_ins_pts = sort(ceil(n*rand(1,2)))

I = rte_ins_pts(1)

J = rte_ins_pts(2)

for k = 1:8 %产生新种群

tmp_pop_rte(k,:) = best_of_8_rte

tmp_pop_brk(k,:) = best_of_8_brk

switch k

case 2% 倒置 *** 作

tmp_pop_rte(k,I:J) =fliplr(tmp_pop_rte(k,I:J))

case 3  % 互换 *** 作

tmp_pop_rte(k,[I J]) =tmp_pop_rte(k,[J I])

case 4 % 滑动平移 *** 作

tmp_pop_rte(k,I:J) =tmp_pop_rte(k,[I+1:J I])

case 5% 更新断点

 tmp_pop_brk(k,:) = randbreaks()

case 6  % 倒置并更新断点

tmp_pop_rte(k,I:J) =fliplr(tmp_pop_rte(k,I:J))

tmp_pop_brk(k,:) =randbreaks()

case 7 % 互换并更新断点

tmp_pop_rte(k,[I J]) =tmp_pop_rte(k,[J I])

tmp_pop_brk(k,:) =randbreaks()

case 8 % 评议并更新断点

tmp_pop_rte(k,I:J) =tmp_pop_rte(k,[I+1:J I])

tmp_pop_brk(k,:) =randbreaks()

otherwise

end

end

new_pop_rte(p-7:p,:) = tmp_pop_rte

new_pop_brk(p-7:p,:) = tmp_pop_brk

end

pop_rte = new_pop_rte

pop_brk = new_pop_brk

end

figure(2)

plot(dist_history,'b','LineWidth',2)

title('历史最优解')

xlabel('迭代次数')

ylabel('最优路程')

% 随机产生一套断点 的集合

function breaks = randbreaks()

if min_tour == 1 % 一个旅行商时，没有断点的设置

tmp_brks = randperm(n-1)

breaks =sort(tmp_brks(1:num_brks))

else % 强制断点至少找到最短的履行长度

num_adjust = find(rand <cum_prob,1)-1

spaces =ceil(num_brks*rand(1,num_adjust))

adjust = zeros(1,num_brks)

for kk = 1:num_brks

adjust(kk) = sum(spaces == kk)

end

breaks = min_tour*(1:num_brks) +cumsum(adjust)

end

end

disp('最优路径为：/n')

disp(opt_rte)

disp('其中断点为为：/n')

disp(opt_brk)

end

1、外推法的MATLAB程序代码如下所示：

function yy = DEWT(f,h,a,b,gama,y0,order,varvec)

%一阶常微绝罩分方程的并搜闹一般表达式的右端函数：f

%积分步长：h

%自变量取值下限：a

%自变量取值上限：b

%外推参数，参考外推公式：gama

%函数初值：y0

%外漏碰推阶数：order

%常微分方程的变量组：varvec

format long；

ArrayH = [1246812162432486496]

N = (b-a)/h

yy = zeros(N+1,1)

for i = 2:N+1

dh = h

s = zeros(order,1)

for j=1:order

dh = h/ArrayH(j) %不同的h值

tmpY = DELGKT2_suen(f,dh,a,a+(i-1)*h,y0,varvec) %休恩法

s(j) = tmpY((i-1)*ArrayH(j)+1)

end

tmpS = zeros(order,1)

for j=1:order-1

for k=(j+1):order

tmpS(k) = s(k)+(s(k)-s(k-1))/((ArrayH(k)/ArrayH(j))^gama-1)

end

s(1:(order-j)) = tmpS((j+1):order) %取对角值

end

yy(i) = tmpS(order)

end

format short

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