matlab 各种智能优化算法

1. 优化算法相关

蚁群优化算法（ACO）

蚁群优化算法是一种模拟蚂蚁觅食行为的优化技术。以下是一个简化版的ACO用于解决旅行商问题（TSP）的MATLAB代码：

function [bestRoute, minDist] = acoTsp(distMatrix, numAnts, numIterations)% 初始化参数nCities = size(distMatrix, 1);pheromone = ones(nCities) / nCities;alpha = 1; beta = 5; rho = 0.5;bestRoute = [];minDist = inf;for iter = 1:numIterationsroutes = cell(numAnts, 1);distances = zeros(numAnts, 1);for ant = 1:numAntsroute = randperm(nCities);distance = calculateRouteDistance(route, distMatrix);% 更新最佳路径if distance < minDistminDist = distance;bestRoute = route;endroutes{ant} = route;distances(ant) = distance;end% 更新信息素updatePheromones(routes, distances, pheromone, rho, alpha, beta);end
endfunction distance = calculateRouteDistance(route, distMatrix)nCities = length(route);distance = sum(distMatrix(sub2ind(size(distMatrix), route(1:end-1), route(2:end)))) + ...distMatrix(route(end), route(1));
endfunction pheromone = updatePheromones(routes, distances, pheromone, rho, alpha, beta)nCities = size(pheromone, 1);deltaPheromone = zeros(nCities);for i = 1:length(routes)route = routes{i};for j = 1:(length(route)-1)deltaPheromone(route(j), route(j+1)) = deltaPheromone(route(j), route(j+1)) + 1/distances(i);endendpheromone = (1-rho) * pheromone + deltaPheromone.^alpha .* (1./distances').^beta;
end

2. 控制器相关

ADRC控制

自抗扰控制器（ADRC）通过估计并补偿系统中的总扰动来增强系统的鲁棒性。下面是一个简单的ADRC控制器设计的MATLAB代码框架：

function u = adrc_control(x, x_dot, r, e, e_dot, b0, h)% 参数设置kp = 10; kd = 5; w_c = 20;% 状态观测器设计z1 = x;z2 = x_dot + e/h;% 扰动估计f_hat = z2 - x_dot;% 控制律v = kp*(r-z1) + kd*(0-e)/h;u = (v-f_hat)/b0;
end

参考 matlab 各种智能优化算法

3. 神经网络相关

BP神经网络

BP神经网络是一种基于误差反向传播算法训练的多层前馈神经网络。以下是在MATLAB中使用trainNetwork函数训练一个简单的BP神经网络的例子：

% 数据准备
X = rand(10, 100); % 输入数据
Y = rand(5, 100); % 输出目标% 定义网络架构
layers = [featureInputLayer(10)fullyConnectedLayer(20)reluLayerfullyConnectedLayer(5)regressionLayer];% 设置训练选项
options = trainingOptions('adam', ...'MaxEpochs', 100, ...'MiniBatchSize', 10, ...'InitialLearnRate', 0.01);% 训练网络
net = trainNetwork(X, Y, layers, options);% 使用训练好的网络进行预测
YPred = predict(net, X);

以上提供的代码片段仅作为入门指导，实际应用中可能需要根据具体情况调整参数和模型结构。希望这些示例能够帮助你更好地理解和实现各种算法。