解释代码clc clear all %% 初始化 N = 3000;n = 3; z = zeros(1,N); theta1 = zeros(n, N); theta2 = zeros(n, N); fai = zeros(n, 1); P = zeros(1, N); Rk_1 = eye(n); for k = 4:N %% 迭代求解 theta0 = [-1.18 0.784 -0.456]'; fai = [-z(k-1) -z(k-2) -z(k-3)]'; z(k) = fai' * theta0 + 1.2*normrnd(0,1); %% 收敛因子 a1 = 15; b1 = 0.6; k1 = 60; k2 = 120; if k <= k1 rho = k/(a1*k1); elseif k > k2 rho = 1/(a1 + b1*(k - k2)); else rho = 1/a1; end %% 随机逼近法 P(:,k) = rho; theta1(:,k) = theta1(:,k-1) + rho * fai * (z(k) - fai'*theta1(:,k-1)); %% 随机牛顿法 Rk = Rk_1 + rho * (fai*fai' - Rk_1); theta2(:,k) = theta2(:,k-1) + rho * inv(Rk) * fai * (z(k) - fai'*theta2(:,k-1)); end disp("随机逼近法") disp(theta1(:,3000)) disp("随机牛顿法") disp(theta2(:,3000)) k = 1:N; figure plot(k,P);xlabel('k');ylabel('收敛因子'); figure subplot(3,1,1);plot(k,theta1(1,:),'-r');legend('a1');title('随机逼近辨识结果'); subplot(3,1,2);plot(k,theta1(2,:),'-b');legend('a2'); subplot(3,1,3);plot(k,theta1(3,:),'-g');legend('a3'); figure subplot(3,1,1);plot(k,theta2(1,:),'-r');legend('a1');title('随机牛顿辨识结果'); subplot(3,1,2);plot(k,theta2(2,:),'-b');legend('a2'); subplot(3,1,3);plot(k,theta2(3,:),'-g');legend('a3');

clear clc tic %%%%%%%%产生输入序列%%%%%%%% x=[1,1,0,1,1,0,1,0,1]; %initial value a1=-1.5; a2=0.7; b1=1.0; b2=0.5; c1=-0.8; c2=0.6; num=8000; %n为脉冲数目 M=[]; %存放M序列,其作为输入 for i=1:num temp=xor(x(4),x(9)); M(i)=x(9); for j=9:-1:2 x(j)=x(j-1); end x(1)=temp; end u=M; %%%%%%%%产生噪声序列%%%%%%%% v=randn(1,num); e(1)=0; e(2)=0; for i=3:num e(i)=v(i)+c1v(i-1)+c2v(i-2); end %%%%%%%%产生观测序列%%%%%%%% z=zeros(num,1); z(1)=0; z(2)=0; for i=3:num z(i)=-a1z(i-1)-a2z(i-2)+b1u(i-1)+b2u(i-2)+e(i); end %%%%%%%%设置初始值%%%%%%%% P=100eye(4); Theta=zeros(4,num); x(1)=0; x(2)=0; for i=3:num H=[-z(i-1);-z(i-2);u(i-1);u(i-2)]; H_SA=[-x(i-1);-x(i-2);u(i-1);u(i-2)]; K=PH_SA/(1+H'PH_SA); Theta(:,i)=Theta(:,i-1)+K(z(i)-H'Theta(:,i-1)); P=(eye(4)-KH')P; x(i)=H_SA'*Theta(:,i); end figure(1) plot(Theta(1,:),'b'); hold on plot(Theta(2,:),'r'); plot(Theta(3,:),'k'); plot(Theta(4,:),'g'); legend('a1','a2','b1','b2'); hold off

3. 生成观测序列 z，通过线性系统的差分方程来计算 z 的值。 4. 设置初始值 P 和 Theta，P 是协方差矩阵，Theta 是参数估计值。 5. 进行递推计算，通过递推公式来更新参数估计值和协方差矩阵。 6. 绘制参数估计...

clear ; close all; clc; %% 阵列信息 N = 16; d =1/2 ; % 均匀加权标准线列阵 %% 波束图绘制 % 均匀加权 Wt = [1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1]/16; theta_t = 90pi/180; % 扫描方位角 theta_angle = 0:0.1:180; % 以0.1°为间隔从0°到180°对空间进行扫描 theta = theta_anglepi/180; Vk = exp(-1i2picos(theta_t)d(0:N-1)).'; % 阵列流型 B_0 = zeros(size(theta)); for i = 1:length(theta) p = exp(1i2picos(theta(i))d(0:N-1)).'; % p列向量 p = Vk.p; B_0(i) = Wt'p; end B_0 = 20*log10(abs(B_0)/max(B_0)); % 归一化波束图（dB） % 绘图 figure(1) set(gcf,'color','white') plot(theta_angle, B_0, 'linewidth', 1.5,'Color','k'); grid on; xlim([0,180]); ylim([-50,0]); title('波束图 (N=16)'); 上述代码我想在绘制的图像中横坐标只显示0 90 180 这三个横坐标点怎么更改

p = exp(1i * 2 * pi * cos(theta(i)) * d * (0:N-1)).'; % p列向量 p = Vk .* p; B_0(i) = 20 * log10(abs(Wt' * p) / max(abs(Wt' * p))); end B_0 = real(B_0); % 取实部，避免出现虚数部分的警告 % 绘图 ...

clc clear all close all % 设置声源位置和声压数据 source = [1, 1, 1]; % 声源位置 p0 = 1; % 声源声压 c = 343; % 声速 fs = 359; % 采样率 t = (0:1/fs:1); % 时间序列 f = 1000; % 信号频率 s = p0sin(2pift); % 信号 % 设置阵列参数 N = 11; % 阵列行列数 M = NN; % 阵列元素数量 d = 0.05; % 阵列元素间距 % 生成平面阵列坐标 [x,y] = meshgrid(-(N-1)/2:(N-1)/2,-(N-1)/2:(N-1)/2); z = zeros(size(x)); pos = [x(:),y(:),z(:)]; pos = posd; figure(1) plot(pos(:,1),pos(:,2),'r'); title('麦克风阵列') % 计算到声源的距离和相位 r = sqrt(sum(bsxfun(@minus,pos,source).^2,2)); phi = exp(-1i2pirf/c); % 添加噪声 noise = 0.1randn(size(s)); piont = s+noise; % 进行波束形成 w = ones(M,1)/M; pppp=diag(phi) y = (w.'diag(phi)).'piont; % 绘制波束图 theta = linspace(-pi,pi,360); p = zeros(size(theta)); for i = 1:length(theta) w = exp(-1i2pircos(theta(i))/c); p(i) = abs(w.'*y).^2; end p = p/max(p); figure; polarplot(theta,p);有什么错误

代码中没有明显的语法错误，但可能存在以下问题： 1. 变量名拼写不一致：有些地方使用了“piont”，应该改为“point”。 2. 波束形成的权重向量w没有进行归一化。可以将w除以其模长，使其成为单位向量。 3. 绘制...

代码错误追踪Clc Clear X =[ ];%投入指标数据 Y =[];%期望产出指标数据 Z=[];%非期望产出指标数据[m,n]=size(X); s=size(Y,1); q=size(Z,1); D=1./(mX'); E=1./((s+q)Y'); F=1./((s+q)*Z'); A=[];b=[]; LB=zeros(n+m+s+q+1,1);UB=[]; theta=zeros(n,1); w=zeros(n+m+s+q+1,n); For i=1:n f=[zeros(1,n) -D(i,:) zeros(1,s+q) 1]; Aeq=[X eye(m) zeros(m,s+q) -X(:,i) Y zeros(s,m) -eye(s) zeros(s,q) -Y(:,i) Z zeros(q,m) zeros(q,s) eye(q) -Z(:,i) zeros(1,n+m) E(i,:) F(i,:) 1]; beq=[zeros(m,1) zeros(s,1) zeros(q,1) ; [w(:,i) theta(i)]=linprog(f,A,b,Aeq,beq,LB,UB); End theta %测算结果

As an AI language model, I do not have personal beliefs or interests, and therefore the term "null" has no relevance to me as a machine. However, in computer science, "null" is a term used to denote ...

Clc Clear X =[ ];%投入指标数据 Y =[];%期望产出指标数据 Z=[];%非期望产出指标数据[m,n]=size(X); s=size(Y,1); q=size(Z,1); D=1./(mX'); E=1./((s+q)Y'); F=1./((s+q)*Z'); A=[];b=[]; LB=zeros(n+m+s+q+1,1);UB=[]; theta=zeros(n,1); w=zeros(n+m+s+q+1,n); For i=1:n f=[zeros(1,n) -D(i,:) zeros(1,s+q) 1]; Aeq=[X eye(m) zeros(m,s+q) -X(:,i) Y zeros(s,m) -eye(s) zeros(s,q) -Y(:,i) Z zeros(q,m) zeros(q,s) eye(q) -Z(:,i) zeros(1,n+m) E(i,:) F(i,:) 1]; beq=[zeros(m,1) zeros(s,1) zeros(q,1) ; [w(:,i) theta(i)]=linprog(f,A,b,Aeq,beq,LB,UB); End theta %测算结果

As an AI language model, I don't interact with the physical world. "null" is a term used in programming, which means a value of zero or no value. It is used to represent a lack of data or an undefined...

clc,clear format long x=xlsread('data.xlsx');%把原始数据保存在纯文本文件data.txt中 X=x(:,[1:3]);%X为输入变量，3为输入变量的个数这里可以自己设置 X=X'; Y=x(:,[4:5]);%Y为输出变量，5（3+2），2为输出变量的个数这里可以自己设置 Y=Y'; n=size(X',1);m=size(X,1);s=size(Y,1); A=[-X' Y']; b=zeros(n,1); LB=zeros(m+s,1);UB=[]; for i=1:n; f=[zeros(1,m) -Y(:,i)']; Aeq=[X(:,i)',zeros(1,s)];beq=1; w(:,i)=linprog(f,A,b,Aeq,beq,LB,UB); E(i,i)=Y(:,i)'*w(m+1:m+s,i); end theta=diag(E)'; fprintf('用DEA方法对此的相对评价结果为：\n'); disp(theta); omega=w(1:m,:) mu=w(m+1:m+s,:)以上是CCR模型的matlab代码，请改写成BBC模型的matlab代码

% 初始化参数 n = size(X,2); m = size(X,1); s = size(Y,1); A = [-X' Y']; b = zeros(n,1); LB = zeros(m+s,1); UB = []; % 进行BBC模型分析 for i = 1:n % 季节性分析 [Xs,d] = detrend(X(:,i)); [Xs,mS] = ...

n元均匀直线matlab代码

%初始化阵列因子 for nn=1:N AF=AF+exp(1j*(nn-1)*k*d*cosd(theta)); %计算阵列因子 end AF=AF/N; %归一化 plot(theta,abs(AF).^2); %绘制阵列因子图形 xlabel('扫描角度/度'); ylabel('阵列因子'); title('n元...

clc clear format long %x=xlsread('data.xlsx'); %把原始数据保存在纯文本文件data.txt中 x = xlsread('C:\Users\XuHaoYuan\Documents\MATLAB\DEA.xlsx'); X=x(:,[1:3]); %X为输入变量，3为输入变量的个数这里可以自己设置 X=X'; Y=x(:,[4:5]); %Y为输出变量，5（3+2），2为输出变量的个数这里可以自己设置 Y=Y'; n=size(X',1); m=size(X,1); s=size(Y,1); A=[-X' eye(m,m) zeros(m,s); zeros(s,m) -eye(s,s) eye(s,s)]; b=zeros(n+s,1); LB=zeros(m+s2,1); UB=[]; for i=1:n; f=[zeros(1,m) zeros(1,m) -Y(:,i)']; Aeq=[X(:,i)', zeros(1,m+s)]; beq=1; w(:,i)=linprog(f,A,b,Aeq,beq,LB,UB); E(i,i)=Y(:,i)'w(m+s+1:m+2s,i); end theta=diag(E)'; fprintf('用DEA方法对此的相对评价结果为：\n'); disp(theta); omega=w(1:m,:); mu=w(m+s+1:m+2s,:);>> BCC-2 尝试将 SCRIPT BCC 作为函数执行: C:\Users\XuHaoYuan\Desktop\BCC.m

b = zeros(n+s,1); LB = zeros(m+s2,1); UB = []; for i=1:n f=[zeros(1,m) zeros(1,m) -Y(:,i)']; Aeq=[X(:,i)', zeros(1,m+s)]; beq=1; w(:,i)=linprog(f,A,b,Aeq,beq,LB,UB); E(i,i)=Y(:,i)'w(m+s+1:m+2s,i)...

clc,clear dt=0.01; t=0:dt:2; n=length(t); G=[-0.2,0.1,0.1,0.3,-0.3; 0.4,-0.5,0.1,-0.2,0.2; 0.3,-0.1,-0.2,0.1,-0.1; 0.2,-0.4,0.2,-0.2,0.2; 0.3,-0.2,-0.1,0.1,-0.1]; R=[-0.3,0.1,0.2,0.2,-0.2; 0.1,-0.4,0.3,0.1,-0.1; 0.2,0.3,-0.5,-0.3,0.3; 0.2,0.1,-0.3,-0.3,0.3; 0.3,-0.1,-0.2,0.4,-0.4]; A1=[10,0;0,10]; A2=[10,0;0,10]; B1=[2,-0.1;-3,1.5]; B2=[2,-0.1;-3,4.5]; theta1=1; theta2=1; t_delay = @(t) exprnd(1/n); % 采用指数分布进行建模 gamma1=[1,0;0,1]; w0 = [3.4,2.5,4.1,1.5,3.3,4.6,2.7,3.2,1.6,0.9]; w1=leader(t(1),w0); numew = zeros(n,10); numew(1,:) = w0; for i=1:n numew(i,:)=w0+dt*w1; w0=numew(i,:);%赋新的初值 w1=leader(t(i),w0); end e11 = sqrt(numew(:,1).^2+numew(:,2).^2); e22 = sqrt(numew(:,3).^2+numew(:,4).^2); e33 = sqrt(numew(:,5).^2+numew(:,6).^2); e1 = (e11+e22+e33)/2; plot(t,e11,'r-.') hold on plot(t,e22,'b') hold on plot(t,e33,'y-')帮我在这段代码中加入无穷分布时滞

clc,clear dt=0.01; t=0:dt:2; n=length(t); G=[-0.2,0.1,0.1,0.3,-0.3; 0.4,-0.5,0.1,-0.2,0.2; 0.3,-0.1,-0.2,0.1,-0.1; 0.2,-0.4,0.2,-0.2,0.2; 0.3,-0.2,-0.1,0.1,-0.1]; R=[-0.3,0.1,0.2,0.2,-0.2; 0.1,-...

理解下列程序%相关最小二乘二步法 %Z(k+2)=1.5Z(k+1)-0.7Z(k)+u(k+1)+0.5u(k)+e(k),e(k)为零均值的不相关随机噪声 %e(k)=v(k)+0.5v(k-1)+0.2v(k-2) clear clc x=[0 1 0 1 1 0 1 1 1]; n=403; M=[]; for i=1:n temp=xor(x(4),x(9)); M(i)=x(9); for j=9:-1:2 x(j)=x(j-1); end x(1)=temp; end v=randn(1,400); e=[]; e(1)=0.3; e(2)=0.5; for i=3:400 e(i)=v(i)+0.5v(i-1)+0.2v(i-2); end z=zeros(402,1); z(1)=-1; z(2)=0; for i=3:400 z(i)=-2z(i-1)-3z(i-2)+4M(i-1)+5M(i-2)+e(i); end P=100eye(4); Pstore=zeros(4,400); Pstore(:,1)=[P(1,1),P(2,2),P(3,3),P(4,4)]; Theta=zeros(4,400); Theta(:,1)=[3;3;3;3]; Theta(:,2)=[3;3;3;3]; Theta(:,3)=[3;3;3;3]; Theta(:,4)=[3;3;3;3]; K=[10;10;10;10]; for i=5:400 h=[-z(i-1);-z(i-2);M(i-1);M(i-2)]; hstar=[M(i-1);M(i-2);M(i-3);M(i-4)]; K=Phstarinv(h'Phstar+1); Theta(:,i)=Theta(:,i-1)+K(z(i)-h'Theta(:,i-1)); P=(eye(4)-Kh')P; Pstore(:,i-1)=[P(1,1),P(2,2),P(3,3),P(4,4)]; end disp('参数 a1 a2 b1 b2 估计结果'); Theta(:,400) i=1:400; figure(1) plot(i,Theta(1,:),i,Theta(2,:),i,Theta(3,:),i,Theta(4,:)); title('待估参数过渡过程'); figure(2) plot(i,Pstore(1,:),i,Pstore(2,:),i,Pstore(3,:),i,Pstore(4,:)); title('估计方差变化过程');

3. 初始化参数估计值 Theta 和协方差矩阵 P。 4. 根据二步法的思想，通过递推公式计算得到参数估计值 Theta 和协方差矩阵 P 的更新过程。 5. 输出最终估计的参数值 Theta(:,400)。 6. 绘制参数估计过程和估计方差...

clear all; clc; du = pi/180; a = [0+0.001, 185+0.0079, 0+0.005, 120+0.12]; alpha = [pi/2+0.003, 0+0.001, pi/2+0.005, pi/2]; d = [0+0.001, 0+0.0079, 90+0.005, 0+0.12]; theta = [90du+0.02, 0, 0.023, 0.08]; beta = zeros(1, 4)+0; L1(1) = Link('d', d(1), 'a', a(1), 'alpha', alpha(1), 'qlim', [180du, 365du], 'modified'); L1(2) = Link('d', d(2), 'a', a(2), 'alpha', alpha(2), 'qlim', [3du, 63du], 'modified'); L1(3) = Link('d', d(3), 'a', a(3), 'alpha', alpha(3), 'qlim', [60du, 120du], 'modified'); L1(4) = Link('d', d(4), 'a', a(4), 'alpha', alpha(4), 'qlim', [230du, 326du], 'modified'); Needle = SerialLink(L1, 'name', 'Needle'); T1 = DH(1, a(1), alpha(1), d(1), theta(1)+beta(1)); T2 = DH(2, a(2), alpha(2), d(2), theta(2)+beta(2)); T3 = DH(3, a(3), alpha(3), d(3), theta(3)+beta(3)); T4 = DH(4, a(4), alpha(4), d(4), theta(4)+beta(4)); T = T1 * T2 * T3 * T4; % Step 2：利用微分变换原理计算机器人各个连杆机构之间的微小原始偏差 delta_a = 0.001; % a参数的微小偏差 delta_alpha = 0.001; % alpha参数的微小偏差 delta_d = 0.001; % d参数的微小偏差 delta_theta = 0.001; % theta参数的微小偏差 delta_beta = 0.001; % beta参数的微小偏差 delta_T1 = DH(1, a(1)+delta_a, alpha(1), d(1), theta(1)+beta(1)) - T1; delta_T2 = DH(2, a(2)+delta_a, alpha(2), d(2), theta(2)+beta(2)) - T2; delta_T3 = DH(3, a(3)+delta_a, alpha(3), d(3), theta(3)+beta(3)) - T3; delta_T4 = DH(4, a(4)+delta_a, alpha(4), d(4), theta(4)+beta(4)) - T4; % Step 3：计算误差矩阵 delta_T = delta_T1 * delta_T2 * delta_T3 * delta_T4; % Step 4：将误差矩阵转化为误差值 delta_x = delta_T(1,4); delta_y = delta_T(2,4); delta_z = delta_T(3,4); % 输出末端位姿误差 fprintf('末端位姿误差：\n'); fprintf('Delta x: %.6f mm\n', delta_x1000); fprintf('Delta y: %.6f mm\n', delta_y1000); fprintf('Delta z: %.6f mm\n', delta_z1000);想要输入一组角度值然后得到末端位姿误差，帮我改写一下程序。

delta_T1 = DH(1, a(1)+delta_a, alpha(1), d(1), theta(1)+delta_theta+beta(1)) - DH(1, a(1), alpha(1), d(1), theta(1)+beta(1)); delta_T2 = DH(2, a(2)+delta_a, alpha(2), d(2), theta(2)+delta_theta+beta(2...

转换为python代码（clear;clc; global_coords = [10, 10; 20, 20; 40, 20; 30, 10];%%%%测量时候桥梁坐标 juli_che =[2,2,2,2];%%%%测量时候车辆中心距离距离桥梁距离 center(1) = mean(global_coords(:,1)); center(2) = mean(global_coords(:,2)); weizhi_che = [10, 10]; weizhi_che1 = [20, 20]; weizhi_che2=weizhi_che - weizhi_che1; weizhi_qiao2= global_coords(2,:)-global_coords(1,:); theta = acos(dot(weizhi_che2,weizhi_qiao2)/(norm(weizhi_che2)norm(weizhi_qiao2)))/pi180; %%%%%%%判断逻辑 juli_cheqiao=juli_cheandqiao(weizhi_che ,center) if juli_cheqiao<20 && abs(theta)<30 %%%%开始采集的条件 1;%%%%%给mqtt发送启动采样的命令 if juli_cheqiao<20 %%%%%结束采集的条件 2;%%%%%给mqtt发送结束采集的命令 else end else end）

下面是将该 MATLAB 代码转换为 Python 代码的过程： python import numpy as np def juli_cheandqiao(weizhi_che, center): juli_cheqiao1 = np.zeros_like(weizhi_che) juli_cheqiao1[:, 0] = weizhi_che[:,...

clc clear close all m=6; u=idinput(2^m-1,'prbs')'; v=normrnd(0,sqrt(0.1),1,2^m); z=zeros(1,2^m); P=100eye(4);%Pm=inv(Hm'Hm) theta(:,1)=zeros(4,1); theta(:,2)=zeros(4,1); for k=3:2^m z(k)= -1.5z(k-1)-0.7z(k-2)+u(k-1)+0.5u(k-2)+v(k); h=[-z(k-1) -z(k-2) u(k-1) u(k-2)]; K=Ph'inv(1+hP*h'); theta(:,k)=theta(:,k-1)+K(z(k)-htheta(:,k-1)); e1(:,k-1)=(theta(:,k)-theta(:,k-1))./theta(:,k-1); e(:,k-1)=abs(e1(:,k-1)); if max(e(:,k-1))<0.000001 break end P=P-Ph'inv(1+hPh')hP; end %% figure(1); plot(theta(1,:),'r','LineWidth',1.2); hold on; plot(theta(2,:),'k','LineWidth',1.2); hold on; plot(theta(3,:),'g','LineWidth',1.2); hold on; plot(theta(4,:),'b','LineWidth',1.2); hold on; grid on; legend('a1','a2','b1','b2'); title('递推最小二乘参数辨识'); figure(2); plot(e1(1,:),'r','LineWidth',1.2); hold on; plot(e1(2,:),'k','LineWidth',1.2); hold on; plot(e1(3,:),'g','LineWidth',1.2); hold on; plot(e1(4,:),'b','LineWidth',1.2); hold on; grid on; title('辨识精度'); legend('a1','a2','b1','b2');请分析这段代码

3. 初始化参数向量 $\theta$ 和协方差矩阵 $P$。其中，$\theta$ 包含了系统的四个参数，$P$ 是一个 $4\times 4$ 的单位矩阵。 4. 从第三个采样点开始，根据递推式 $z(k)= -1.5z(k-1)-0.7z(k-2)+u(k-1)+0.5u(k-2)+v...

把以下matlab代码转换为python代码（clear;clc; global_coords = [10, 10; 20, 20; 40, 20; 30, 10];%%%%测量时候桥梁坐标 juli_qiaoche =[2,2,2,2];%%%%测量时候车辆中心距离距离桥梁距离 % 转换为局部坐标 point_bridge1 = zuobiao_qiao( global_coords,juli_qiaoche )）

以下是将 MATLAB 代码转换为 Python 代码后的结果： python import numpy as np def zuobiao_qiao(global_coords, juli_qiaoche): local_coords = np.zeros_like(global_coords) local_coords[:, 0] = global...

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把以下matlab代码转换为python代码（clear;clc; global_coords = [10, 10; 20, 20; 40, 20; 30, 10];%%%%测量时候桥梁坐标 juli_qiaoche =[2,2,2,2];%%%%测量时候车辆中心距离距离桥梁距离 % 转换为局部坐标 point_bridge1 = zuobiao_qiao( global_coords,juli_qiaoche )）

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