yxb11.m

这个程序是用来模拟人工神经网络的拓扑结构的

%人工神经网络的工作原理
%人工神经网络是由人工建立的以有向图为拓扑结构的动态系统
%基本的神经网络模型
clear all 
close all
clc

%定义的初始变量
L1=0.08;b=1;
L2=0.04;

Ae=2.2*10^(-19);
Ag=8.7*10^(-19);
A21=2.8*10^(-19);
Te=3.4*10^(-6);
T2=230*10^(-6);

%while L1<=0.2
 %l2(b)=L1;
 T0=0.75;
 %while T0<0.80
    %l2(b)=T0;

Tr=2*(L1+L2)*1.83/(3.0*10^10);

%逻辑循环语句
%建立人工神经网络的基本拓扑结构

pp=1;
while pp<=3;
    l2(b)=pp;
A=1.5*10^-4;
%while A<=2.5*10^-4
    %l2(b)=A;
R=0.5*pp/((L1+L2)*A*6.63*10^-34*(3.0*10^8/(808*10^-9)));
a=0.06;
r=0.83;
%while r<=0.96;
    %l2(b)=r;
na=-log(T0)/(Ag*L2);

%定义坐标轴
global m1;
global m2
m2=R;
global m3
m3=1/T2;
global m4
m4=2*A21*L1/Tr;
global m5
m5=[2*a*(L1+L2)-log(r)+0.02]/Tr;
global m6
m6=Ae*L2/Tr;
global m7
m7=na;
global m8
m8=Ag*L2/Tr;
    
global m10
m10=1/Te;
    
  m1=(-2)*r*A21*L1/Tr;
  
   tspan=[0,1.5*10^(-2)];
   y0=[0;0;0];

[t,ff]=ode23tb('check',tspan, y0);
%plot(t*10^9-1.5*10^5,10^-3*6.63*10^-34*(3.0*10^8/(1064*10^-9))*A*(L1+L2)/Tr*log(1/r)*ff(:,2));
%plot(t,ff(:,2))
%plot(t*10^9-132701,ff(:,2))
%fg=ff(:,2);
%plot(t*10^9-693393,10^-3*6.63*10^-34*(3.0*10^8/(1064*10^-9))*A*(L1+L2)/Tr*log(1/r)*ff(:,2));

    %axis([0 10 0 3 ])
	%x=[t ff];
%中心逻辑循环结构
%循环主体
     i=1;
h=0;
while t(i)>=0
    if ff(i,2)>=h
        h=ff(i,2);
        i=i+1;
    elseif ff(i,2)<10000
        i=i+1;
    else
        t1=t(i-1);
        mm=ff(i,2);
        k=i;
        break;
    end 
    
end
i=i-1;

while i>0
   if ff(i,2)<=h
       h=ff(i,2);
      i=i+1; 
  elseif ff(i,2)>0.1
      i=i+1;
  else
     
      break;
  
  end
   
end
hh=i-1;
i=i+k/4;
while i>0
    if ff(i,2)>=h
        h=ff(i,2);
        i=i+1;
        
    elseif ff(i,2)<mm/1.5
        i=i+1;
    else
        
        t2=t(i);
        break
    
    end
end
j=1;
while j<=hh
    hi(j)=t(j);
    gz(j)=ff(j,2);
    j=j+1;
end
m=1;eng=0;ha=diff(hi);

while m<hh
    eng=eng+gz(m)*ha(m);
    m=m+1;
end
    
%plot(t*10^9-1.5*10^5,10^-3*6.63*10^-34*(3.0*10^8/(1064*10^-9))*A*(L1+L2)/Tr*log(1/r)*ff(:,2));
%plot(t,ff(:,2))
%plot(t*10^9-132701,ff(:,2))
%fg=ff(:,2);
%plot(t*10^9-693393,10^-3*6.63*10^-34*(3.0*10^8/(1064*10^-9))*A*(L1+L2)/Tr*log(1/r)*ff(:,2));

    %axis([0 10 0 3 ])
	%x=[t ff];
    
eng;
ppp(b)=1/(2*t1)
%l2(b)=T0;

%plot(t*10^9-1.5*10^5,10^-3*6.63*10^-34*(3.0*10^8/(1064*10^-9))*A*(L1+L2)/Tr*log(1/r)*ff(:,2));
%plot(t,ff(:,2))
%plot(t*10^9-132701,ff(:,2))
%fg=ff(:,2);
%plot(t*10^9-693393,10^-3*6.63*10^-34*(3.0*10^8/(1064*10^-9))*A*(L1+L2)/Tr*log(1/r)*ff(:,2));

    %axis([0 10 0 3 ])
	%x=[t ff];

engg(b)=6.63*10^-34*(3.0*10^8/(1064*10^-9))*A*(L1+L2)/Tr*log(1/r)*eng
feak(b)=6.63*10^-34*(3.0*10^8/(1064*10^-9))*A*(L1+L2)/Tr*log(1/r)*max(ff(k,2))
www(b)=engg/feak
b=b+1;
%L1=L1+0.02;
pp=pp+0.5;
%T0=T0+0.02;
%r=r+0.01;
%A=A+0.2*10^-4;
end
plot(l2,engg);  %画出人工神经网络的最终图像图像，得出最终结果