function [source_x, source_y] = pollution_source_location(x, y, concentration) % x, y, concentration为已知的污染物坐标和浓度 % 定义目标函数，即误差平方和 objective_func = @(p) sum((1 ./ ((x - p(1)).^2 + (y - p(2)).^2) - concentration).^2); % 用fminsearch函数求解最小化误差平方和的污染源位置 initial_guess = [0, 0]; % 初始猜测位置 options = optimset('MaxFunEvals', 1e4); % 设置最大迭代次数 % 定义目标函数，即误差平方和objective_func = @(p) sum((1 ./ sum((bsxfun(@minus, [x;y], p.').^2)) - concentration).^2); % 返回求解结果 source_x = source_xy(1); source_y = source_xy(2); end x = [2.98, 34.1, 2.12, 3.57, 26.08, 4.84, 40.91, 40.88, 36.12, 7.49, 34.63, 45.56]; y = [52.77, 41.49, 77.84, 51.92, 64.03, 36.3, 34.59, 66.03, 6.68, 10.65, 23.34, 12.45]; concentration = [3.47377698638037, 4.21048700181696, 2.32995431278789, 3.54137828156852, 3.34274420916079, 4.55904162880661, 3.62027053282848, 2.84536759532812, 2.60982213752012, 2.82210664759122, 3.45, 2.98]; [source_x, source_y] = pollution_source_location(x, y, concentration); fprintf('污染源位置：(%f, %f) m\n', source_x, source_y);优化一下次代码使其可以运行

以下是优化后的代码：

function [source_x, source_y] = pollution_source_location(x, y, concentration)
% x, y, concentration为已知的污染物坐标和浓度

% 定义目标函数，即误差平方和
objective_func = @(p) sum((1 ./ sum((bsxfun(@minus, [x;y], p.').^2)) - concentration).^2);

% 用fminsearch函数求解最小化误差平方和的污染源位置
initial_guess = [0, 0]; % 初始猜测位置
options = optimset('MaxFunEvals', 1e4); % 设置最大迭代次数
source_xy = fminsearch(objective_func, initial_guess, options);

% 返回求解结果
source_x = source_xy(1);
source_y = source_xy(2);
end

x = [2.98, 34.1, 2.12, 3.57, 26.08, 4.84, 40.91, 40.88, 36.12, 7.49, 34.63, 45.56];
y = [52.77, 41.49, 77.84, 51.92, 64.03, 36.3, 34.59, 66.03, 6.68, 10.65, 23.34, 12.45];
concentration = [3.47377698638037, 4.21048700181696, 2.32995431278789, 3.54137828156852, 3.34274420916079, 4.55904162880661, 3.62027053282848, 2.84536759532812, 2.60982213752012, 2.82210664759122, 3.45, 2.98];

[source_x, source_y] = pollution_source_location(x, y, concentration);
fprintf('污染源位置：(%f, %f) m\n', source_x, source_y);

注意，需要将以上代码保存为 MATLAB 文件，并在 MATLAB 中运行。