利用matlab分别对三边测量定位算法和改进算法进行仿真和验证

利用matlab分别对三边测量定位算法和改进算法进行仿真和验证

%% 清空环境变量clcclearload data%% 数据累加作为网络输入[n,m]=size(X);for i=1:n    y(i,1)=sum(X(1:i,1));    y(i,2)=sum(X(1:i,2));    y(i,3)=sum(X(1:i,3));    y(i,4)=sum(X(1:i,4));    y(i,5)=sum(X(1:i,5));    y(i,6)=sum(X(1:i,6));end%% 网络参数初始化a=0.3+rand(1)/4;b1=0.3+rand(1)/4;b2=0.3+rand(1)/4;b3=0.3+rand(1)/4;b4=0.3+rand(1)/4;b5=0.3+rand(1)/4;%% 学习速率初始化u1=0.0015;u2=0.0015;u3=0.0015;u4=0.0015;u5=0.0015;%% 权值阀值初始化t=1;w11=a;w21=-y(1,1);w22=2*b1/a;w23=2*b2/a;w24=2*b3/a;w25=2*b4/a;w26=2*b5/a;w31=1+exp(-a*t);w32=1+exp(-a*t);w33=1+exp(-a*t);w34=1+exp(-a*t);w35=1+exp(-a*t);w36=1+exp(-a*t);theta=(1+exp(-a*t))*(b1*y(1,2)/a+b2*y(1,3)/a+b3*y(1,4)/a+b4*y(1,5)/a+b5*y(1,6)/a-y(1,1));kk=1;%% 循环迭代for j=1:10%循环迭代E(j)=0;for i=1:30        %% 网络输出计算    t=i;    LB_b=1/(1+exp(-w11*t));   %LB层输出    LC_c1=LB_b*w21;           %LC层输出    LC_c2=y(i,2)*LB_b*w22;    %LC层输出    LC_c3=y(i,3)*LB_b*w23;    %LC层输出    LC_c4=y(i,4)*LB_b*w24;    %LC层输出    LC_c5=y(i,5)*LB_b*w25;    %LC层输出    LC_c6=y(i,6)*LB_b*w26;    %LC层输出     LD_d=w31*LC_c1+w32*LC_c2+w33*LC_c3+w34*LC_c4+w35*LC_c5+w36*LC_c6;    %LD层输出    theta=(1+exp(-w11*t))*(w22*y(i,2)/2+w23*y(i,3)/2+w24*y(i,4)/2+w25*y(i,5)/2+w26*y(i,6)/2-y(1,1));   %阀值    ym=LD_d-theta;   %网络输出值    yc(i)=ym;        %% 权值修正    error=ym-y(i,1);      %计算误差    E(j)=E(j)+abs(error);    %误差求和           error1=error*(1+exp(-w11*t));     %计算误差    error2=error*(1+exp(-w11*t));     %计算误差    error3=error*(1+exp(-w11*t));    error4=error*(1+exp(-w11*t));    error5=error*(1+exp(-w11*t));    error6=error*(1+exp(-w11*t));    error7=(1/(1+exp(-w11*t)))*(1-1/(1+exp(-w11*t)))*(w21*error1+w22*error2+w23*error3+w24*error4+w25*error5+w26*error6);        %修改权值    w22=w22-u1*error2*LB_b;    w23=w23-u2*error3*LB_b;    w24=w24-u3*error4*LB_b;    w25=w25-u4*error5*LB_b;    w26=w26-u5*error6*LB_b;    w11=w11+a*t*error7;endend  %画误差随进化次数变化趋势figure(1)plot(E)title('训练误差','fontsize',12);xlabel('进化次数','fontsize',12);ylabel('误差','fontsize',12);%print -dtiff -r600 28-3%根据训出的灰色神经网络进行预测for i=31:36    t=i;    LB_b=1/(1+exp(-w11*t));   %LB层输出    LC_c1=LB_b*w21;           %LC层输出    LC_c2=y(i,2)*LB_b*w22;    %LC层输出    LC_c3=y(i,3)*LB_b*w23;    %LC层输出    LC_c4=y(i,4)*LB_b*w24;    %LC层输出    LC_c5=y(i,5)*LB_b*w25;    LC_c6=y(i,6)*LB_b*w26;    LD_d=w31*LC_c1+w32*LC_c2+w33*LC_c3+w34*LC_c4+w35*LC_c5+w36*LC_c6;    %LD层输出    theta=(1+exp(-w11*t))*(w22*y(i,2)/2+w23*y(i,3)/2+w24*y(i,4)/2+w25*y(i,5)/2+w26*y(i,6)/2-y(1,1));   %阀值    ym=LD_d-theta;   %网络输出值    yc(i)=ym;endyc=yc*100000;y(:,1)=y(:,1)*10000;%计算预测的每月需求量for j=36:-1:2    ys(j)=(yc(j)-yc(j-1))/10;endfigure(2)plot(ys(31:36),'-*');hold onplot(X(31:36,1)*10000,'r:o');