import pandas as pd # 导入pandas工具库 import matplotlib.pyplot as plt pd.set_option('display.max_columns', 1000) pd.set_option('display.max_rows', 1000) pd.set_option('display.max_colwidth', 1000) import numpy as np import matplotlib.pyplot as plt """ Q 系统噪声 R 测量噪声 X(k|k-1) 上一次状态预测结果 X(k-1|k-1) 上一时刻的最优预测值 P(k|k-1) X(k|k-1)对应的convariance协方差 P(k-1|k-1) X(k-1|k-1) 对应的convariance协方差 """ x_last = 0 p_last = 0 Q = 0.01 # 系统噪声 R = 0.5 # 测量噪声 def kalman(z_measure, x_last=0, p_last=0, Q=0.018, R=0.0542): x_mid = x_last p_mid = p_last + Q kg = p_mid / (p_mid + R) x_now = x_mid + kg * (z_measure - x_mid) p_now = (1 - kg) * p_mid p_last = p_now x_last = x_now return x_now, p_last, x_last path = 'C:/Users/asus/Desktop/第4章作业/卡尔曼滤波数据.xlsx' data_B = pd.read_excel(path, header=None) for j in range(8): xj = list(data_B.iloc[::, j]) yj=[] for i in range(len(xj)): pred, p_last, x_last = kalman(xj[i], x_last, p_last, Q, R) yj.append(pred) ax1 = plt.subplot(1,2,1) ax2 = plt.subplot(1,2,2) plt.sca(ax1) plt.plot(xj, color="g") # 测量值 plt.sca(ax2) plt.plot(yj, color="r") # 预测值 plt.show() 把上面的pyton代码转为matlab代码

以下是将上述Python代码转换为MATLAB代码的版本：

% 导入pandas工具库
% 设置显示选项
format long
x_last = 0;
p_last = 0;
Q = 0.01; % 系统噪声
R = 0.5; % 测量噪声

function [x_now, p_last, x_last] = kalman(z_measure, x_last, p_last, Q, R)
    x_mid = x_last;
    p_mid = p_last + Q;
    kg = p_mid / (p_mid + R);
    x_now = x_mid + kg * (z_measure - x_mid);
    p_now = (1 - kg) * p_mid;
    p_last = p_now;
    x_last = x_now;
end

path = 'C:\Users\asus\Desktop\第4章作业\卡尔曼滤波数据.xlsx';
data_B = xlsread(path);

figure;
for j = 1:8
    xj = data_B(:, j);
    yj = zeros(size(xj));
    for i = 1:length(xj)
        [pred, p_last, x_last] = kalman(xj(i), x_last, p_last, Q, R);
        yj(i) = pred;
    end
    
    subplot(1,2,1);
    plot(xj, 'g'); hold on; % 测量值
    subplot(1,2,2);
    plot(yj, 'r'); hold on; % 预测值
end

hold off;

请注意，这段MATLAB代码的绘图部分使用了`subplot`和`hold`函数来创建多个