topsis带有权重的MATLAB代码

Sure, here is an example of TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) implementation in MATLAB with weightings:

function [bestAlternative scores] = topsis(weights, data)
    % Normalize the data
    normalizedData = normalizeData(data);

    % Calculate weighted normalized decision matrix
    weightedNormalizedData = bsxfun(@times, normalizedData, weights);

    % Determine the positive and negative ideal solutions
    positiveIdealSolution = max(weightedNormalizedData, [], 1);
    negativeIdealSolution = min(weightedNormalizedData, [], 1);

    % Calculate the Euclidean distances to positive and negative ideal solutions
    positiveDistances = sqrt(sum((weightedNormalizedData - positiveIdealSolution).^2, 2));
    negativeDistances = sqrt(sum((weightedNormalizedData - negativeIdealSolution).^2, 2));

    % Calculate the relative closeness to the ideal solution
    scores = negativeDistances ./ (positiveDistances + negativeDistances);

    % Find the best alternative (highest score)
    [~, bestAlternative] = max(scores);
end

function normalizedData = normalizeData(data)
    % Normalize the data (range: 0 to 1)
    normalizedData = (data - min(data)) ./ (max(data) - min(data));
end

To use the code, you need to provide the weights and data as input to the `topsis` function. The `weights` parameter should be a vector containing the weights for each criterion, and the `data` parameter should be a matrix where each row represents an alternative and each column represents a criterion. The code will return the index of the best alternative and the scores for all alternatives.

Note that this code assumes that the criteria are to be maximized. If you have criteria that need to be minimized, you can modify the code accordingly.

Hope this helps! Let me know if you have any further questions.