MATLAB多隐含层极限学习机(ML-ELM) - 大数据处理

MATLAB多隐含层极限学习机实现，特别优化用于大规模数据处理。

1. 核心ML-ELM类实现

ML_ELM.m - 主类文件

classdef ML_ELM < handle
    % 多隐含层极限学习机 - 适用于大数据处理
    % Multi-Layer Extreme Learning Machine for Big Data
    
    properties
        % 网络结构参数
        hidden_layers      % 各层神经元数量 [layer1_neurons, layer2_neurons, ...]
        activation         % 激活函数类型
        C                  % 正则化参数
        batch_size         % 批处理大小
        
        % 网络参数
        input_weights      % 输入权重矩阵
        biases             % 偏置向量
        output_weights     % 输出权重矩阵
        
        % 训练信息
        training_time      % 训练时间
        training_error     % 训练误差
    end
    
    methods
        function obj = ML_ELM(hidden_layers, varargin)
            % 构造函数
            % 参数:
            %   hidden_layers - 各层神经元数量数组
            %   'Activation' - 激活函数 ('sigmoid', 'relu', 'tanh')
            %   'C' - 正则化参数
            %   'BatchSize' - 批处理大小
            
            p = inputParser;
            addRequired(p, 'hidden_layers', @(x) isnumeric(x) && all(x > 0));
            addParameter(p, 'Activation', 'sigmoid', @ischar);
            addParameter(p, 'C', 1, @(x) isnumeric(x) && x > 0);
            addParameter(p, 'BatchSize', 1000, @(x) isnumeric(x) && x > 0);
            
            parse(p, hidden_layers, varargin{:});
            
            obj.hidden_layers = p.Results.hidden_layers;
            obj.activation = p.Results.Activation;
            obj.C = p.Results.C;
            obj.batch_size = p.Results.BatchSize;
            
            fprintf('ML-ELM初始化完成:\n');
            fprintf('  网络结构: 输入 -> %s -> 输出\n', mat2str(obj.hidden_layers));
            fprintf('  激活函数: %s\n', obj.activation);
            fprintf('  正则化参数: %.4f\n', obj.C);
            fprintf('  批处理大小: %d\n', obj.batch_size);
        end
        
        function initialize_weights(obj, input_dim, output_dim)
            % 初始化网络权重
            fprintf('初始化网络权重...\n');
            
            n_layers = length(obj.hidden_layers);
            obj.input_weights = cell(1, n_layers);
            obj.biases = cell(1, n_layers);
            
            % 初始化第一层
            obj.input_weights{1} = randn(input_dim, obj.hidden_layers(1)) * 0.1;
            obj.biases{1} = randn(1, obj.hidden_layers(1)) * 0.1;
            
            % 初始化隐藏层
            for i = 2:n_layers
                obj.input_weights{i} = randn(obj.hidden_layers(i-1), obj.hidden_layers(i)) * 0.1;
                obj.biases{i} = randn(1, obj.hidden_layers(i)) * 0.1;
            end
            
            % 初始化输出权重
            obj.output_weights = randn(obj.hidden_layers(end), output_dim) * 0.1;
        end
        
        function H = activation_function(obj, X)
            % 激活函数
            switch obj.activation
                case 'sigmoid'
                    H = 1 ./ (1 + exp(-max(min(X, 50), -50))); % 防止溢出
                case 'relu'
                    H = max(0, X);
                case 'tanh'
                    H = tanh(X);
                case 'leaky_relu'
                    H = max(0.01 * X, X);
                otherwise
                    error('不支持的激活函数: %s', obj.activation);
            end
        end
        
        function [H_final, layer_outputs] = forward_pass(obj, X)
            % 前向传播
            n_layers = length(obj.hidden_layers);
            layer_outputs = cell(1, n_layers);
            
            current_output = X;
            
            for i = 1:n_layers
                % 计算当前层输入
                layer_input = current_output * obj.input_weights{i} + ...
                             repmat(obj.biases{i}, size(current_output, 1), 1);
                
                % 激活函数
                current_output = obj.activation_function(layer_input);
                layer_outputs{i} = current_output;
            end
            
            H_final = current_output;
        end
        
        function train(obj, X, Y)
            % 训练ML-ELM网络
            % X: 输入数据 (n_samples x n_features)
            % Y: 目标输出 (n_samples x n_classes) - 对于分类应为one-hot编码
            
            fprintf('开始训练ML-ELM...\n');
            t_start = tic;
            
            [n_samples, input_dim] = size(X);
            output_dim = size(Y, 2);
            
            % 初始化权重
            obj.initialize_weights(input_dim, output_dim);
            
            % 逐层训练
            n_layers = length(obj.hidden_layers);
            current_features = X;
            
            for layer = 1:n_layers
                fprintf('训练第 %d 层 (%d 个神经元)...\n', layer, obj.hidden_layers(layer));
                
                if layer == 1
                    input_dim_layer = input_dim;
                else
                    input_dim_layer = obj.hidden_layers(layer-1);
                end
                
                % 计算当前层输出
                H_layer = obj.compute_layer_output_batch(current_features, layer);
                
                if layer < n_layers
                    % 对于隐藏层，输出作为下一层的输入
                    current_features = H_layer;
                else
                    % 对于最后一层，计算输出权重
                    fprintf('计算输出权重...\n');
                    obj.compute_output_weights(H_layer, Y);
                end
            end
            
            obj.training_time = toc(t_start);
            
            % 计算训练误差
            Y_pred = obj.predict(X);
            if size(Y, 2) == 1
                % 回归问题
                obj.training_error = mean((Y_pred - Y).^2);
                fprintf('训练完成! 时间: %.2f秒, MSE: %.6f\n', ...
                    obj.training_time, obj.training_error);
            else
                % 分类问题
                [~, Y_true] = max(Y, [], 2);
                [~, Y_pred] = max(Y_pred, [], 2);
                accuracy = sum(Y_true == Y_pred) / n_samples;
                obj.training_error = 1 - accuracy;
                fprintf('训练完成! 时间: %.2f秒, 准确率: %.4f\n', ...
                    obj.training_time, accuracy);
            end
        end
        
        function H_layer = compute_layer_output_batch(obj, X, layer_idx)
            % 批处理计算层输出 - 适用于大数据
            n_samples = size(X, 1);
            n_batches = ceil(n_samples / obj.batch_size);
            
            H_layer = zeros(n_samples, obj.hidden_layers(layer_idx));
            
            for batch = 1:n_batches
                start_idx = (batch-1) * obj.batch_size + 1;
                end_idx = min(batch * obj.batch_size, n_samples);
                
                batch_data = X(start_idx:end_idx, :);
                
                % 计算当前batch的隐藏层输出
                layer_input = batch_data * obj.input_weights{layer_idx} + ...
                             repmat(obj.biases{layer_idx}, size(batch_data, 1), 1);
                H_batch = obj.activation_function(layer_input);
                
                H_layer(start_idx:end_idx, :) = H_batch;
                
                if mod(batch, 10) == 0
                    fprintf('  批处理进度: %d/%d\n', batch, n_batches);
                end
            end
        end
        
        function compute_output_weights(obj, H, Y)
            % 计算输出权重
            n_samples = size(H, 1);
            
            if n_samples < obj.hidden_layers(end)
                % 样本数少于特征数，使用公式1
                obj.output_weights = (H' * H + eye(obj.hidden_layers(end)) / obj.C) \ (H' * Y);
            else
                % 样本数多于特征数，使用公式2（更高效）
                obj.output_weights = (eye(obj.hidden_layers(end)) / obj.C + H' * H) \ (H' * Y);
            end
        end
        
        function Y_pred = predict(obj, X)
            % 预测
            H_final = obj.forward_pass_batch(X);
            Y_pred = H_final * obj.output_weights;
        end
        
        function H_final = forward_pass_batch(obj, X)
            % 批处理前向传播 - 适用于大数据预测
            n_samples = size(X, 1);
            n_batches = ceil(n_samples / obj.batch_size);
            n_layers = length(obj.hidden_layers);
            
            H_final = zeros(n_samples, obj.hidden_layers(end));
            
            for batch = 1:n_batches
                start_idx = (batch-1) * obj.batch_size + 1;
                end_idx = min(batch * obj.batch_size, n_samples);
                
                batch_data = X(start_idx:end_idx, :);
                
                % 逐层前向传播
                current_output = batch_data;
                for layer = 1:n_layers
                    layer_input = current_output * obj.input_weights{layer} + ...
                                 repmat(obj.biases{layer}, size(current_output, 1), 1);
                    current_output = obj.activation_function(layer_input);
                end
                
                H_final(start_idx:end_idx, :) = current_output;
            end
        end
    end
end

2. 大数据处理工具函数

data_utils.m - 数据处理工具

classdef data_utils
    % 数据处理工具函数 - 专门针对大数据优化
    
    methods (Static)
        
        function [X_train, Y_train, X_test, Y_test] = train_test_split(X, Y, test_size)
            % 数据集分割
            n_samples = size(X, 1);
            n_test = round(n_samples * test_size);
            
            rng(42); % 固定随机种子保证可重复性
            indices = randperm(n_samples);
            
            test_indices = indices(1:n_test);
            train_indices = indices(n_test+1:end);
            
            X_train = X(train_indices, :);
            Y_train = Y(train_indices, :);
            X_test = X(test_indices, :);
            X_test = X(test_indices, :);
            Y_test = Y(test_indices, :);
        end
        
        function X_normalized = normalize_data(X, method)
            % 数据标准化
            % method: 'minmax', 'zscore', 'none'
            
            if nargin < 2
                method = 'minmax';
            end
            
            switch method
                case 'minmax'
                    % 最小-最大标准化 [0,1]
                    min_vals = min(X, [], 1);
                    max_vals = max(X, [], 1);
                    range_vals = max_vals - min_vals;
                    range_vals(range_vals == 0) = 1; % 防止除零
                    
                    X_normalized = (X - min_vals) ./ range_vals;
                    
                case 'zscore'
                    % Z-score标准化
                    mu = mean(X, 1);
                    sigma = std(X, 0, 1);
                    sigma(sigma == 0) = 1; % 防止除零
                    
                    X_normalized = (X - mu) ./ sigma;
                    
                case 'none'
                    X_normalized = X;
                    
                otherwise
                    error('不支持的标准化方法: %s', method);
            end
        end
        
        function Y_encoded = onehot_encode(Y)
            % One-hot编码
            classes = unique(Y);
            n_classes = length(classes);
            n_samples = length(Y);
            
            Y_encoded = zeros(n_samples, n_classes);
            
            for i = 1:n_classes
                Y_encoded(Y == classes(i), i) = 1;
            end
        end
        
        function save_large_data(data, filename)
            % 保存大数据 - 使用MAT文件格式
            fprintf('保存数据到 %s...\n', filename);
            save(filename, 'data', '-v7.3'); % -v7.3 支持大于2GB的文件
        end
        
        function data = load_large_data(filename)
            % 加载大数据
            fprintf('从 %s 加载数据...\n', filename);
            loaded = load(filename);
            data = loaded.data;
        end
        
        function [X_batches, Y_batches] = create_batches(X, Y, batch_size)
            % 创建数据批次
            n_samples = size(X, 1);
            n_batches = ceil(n_samples / batch_size);
            
            X_batches = cell(n_batches, 1);
            Y_batches = cell(n_batches, 1);
            
            for i = 1:n_batches
                start_idx = (i-1) * batch_size + 1;
                end_idx = min(i * batch_size, n_samples);
                
                X_batches{i} = X(start_idx:end_idx, :);
                if ~isempty(Y)
                    Y_batches{i} = Y(start_idx:end_idx, :);
                end
            end
        end
    end
end

3. 性能评估工具

evaluation_metrics.m - 评估指标

classdef evaluation_metrics
    % 模型评估指标
    
    methods (Static)
        
        function accuracy = classification_accuracy(Y_true, Y_pred)
            % 分类准确率
            [~, true_labels] = max(Y_true, [], 2);
            [~, pred_labels] = max(Y_pred, [], 2);
            
            accuracy = sum(true_labels == pred_labels) / length(true_labels);
        end
        
        function [precision, recall, f1] = classification_metrics(Y_true, Y_pred)
            % 分类评估指标
            [~, true_labels] = max(Y_true, [], 2);
            [~, pred_labels] = max(Y_pred, [], 2);
            
            n_classes = size(Y_true, 2);
            precision = zeros(n_classes, 1);
            recall = zeros(n_classes, 1);
            f1 = zeros(n_classes, 1);
            
            for i = 1:n_classes
                true_positive = sum((true_labels == i) & (pred_labels == i));
                false_positive = sum((true_labels ~= i) & (pred_labels == i));
                false_negative = sum((true_labels == i) & (pred_labels ~= i));
                
                if (true_positive + false_positive) > 0
                    precision(i) = true_positive / (true_positive + false_positive);
                end
                
                if (true_positive + false_negative) > 0
                    recall(i) = true_positive / (true_positive + false_negative);
                end
                
                if (precision(i) + recall(i)) > 0
                    f1(i) = 2 * precision(i) * recall(i) / (precision(i) + recall(i));
                end
            end
        end
        
        function mse = mean_squared_error(Y_true, Y_pred)
            % 均方误差
            mse = mean((Y_true - Y_pred).^2);
        end
        
        function r2 = r_squared(Y_true, Y_pred)
            % R平方
            ss_res = sum((Y_true - Y_pred).^2);
            ss_tot = sum((Y_true - mean(Y_true)).^2);
            r2 = 1 - (ss_res / ss_tot);
        end
        
        function plot_training_curves(metrics_history)
            % 绘制训练曲线
            figure('Position', [100, 100, 1200, 800]);
            
            if isfield(metrics_history, 'accuracy')
                % 分类问题
                subplot(2, 2, 1);
                plot(metrics_history.accuracy, 'b-', 'LineWidth', 2);
                xlabel('迭代次数');
                ylabel('准确率');
                title('训练准确率');
                grid on;
                
                subplot(2, 2, 2);
                plot(metrics_history.loss, 'r-', 'LineWidth', 2);
                xlabel('迭代次数');
                ylabel('损失');
                title('训练损失');
                grid on;
            else
                % 回归问题
                subplot(1, 2, 1);
                plot(metrics_history.mse, 'b-', 'LineWidth', 2);
                xlabel('迭代次数');
                ylabel('MSE');
                title('均方误差');
                grid on;
                
                subplot(1, 2, 2);
                plot(metrics_history.r2, 'g-', 'LineWidth', 2);
                xlabel('迭代次数');
                ylabel('R²');
                title('决定系数');
                grid on;
            end
        end
    end
end

4. 示例和使用演示

demo_ml_elm.m - 演示程序

%% ML-ELM 大数据处理演示
clear; clc; close all;

fprintf('=== MATLAB多隐含层极限学习机大数据处理演示 ===\n\n');

%% 1. 生成示例大数据
fprintf('1. 生成示例数据...\n');
rng(42); % 固定随机种子

% 生成大规模数据集
n_samples = 50000;
n_features = 100;
n_classes = 5;

% 生成随机数据
X = randn(n_samples, n_features);
Y = randi([1, n_classes], n_samples, 1);

% 转换为one-hot编码
Y_onehot = data_utils.onehot_encode(Y);

% 数据标准化
X_normalized = data_utils.normalize_data(X, 'zscore');

% 分割数据集
[X_train, Y_train, X_test, Y_test] = data_utils.train_test_split(...
    X_normalized, Y_onehot, 0.2);

fprintf('   训练集: %d 样本 x %d 特征\n', size(X_train));
fprintf('   测试集: %d 样本 x %d 特征\n', size(X_test));
fprintf('   类别数: %d\n', n_classes);

%% 2. 创建并训练ML-ELM模型
fprintf('\n2. 创建ML-ELM模型...\n');

% 定义网络结构
hidden_layers = [200, 100, 50];  % 3个隐藏层

% 创建ML-ELM模型
ml_elm = ML_ELM(hidden_layers, ...
    'Activation', 'sigmoid', ...
    'C', 1, ...
    'BatchSize', 2000);

% 训练模型
fprintf('\n开始训练...\n');
ml_elm.train(X_train, Y_train);

%% 3. 模型评估
fprintf('\n3. 模型评估...\n');

% 训练集预测
fprintf('   训练集预测...\n');
Y_train_pred = ml_elm.predict(X_train);
train_accuracy = evaluation_metrics.classification_accuracy(Y_train, Y_train_pred);
fprintf('   训练集准确率: %.4f\n', train_accuracy);

% 测试集预测
fprintf('   测试集预测...\n');
Y_test_pred = ml_elm.predict(X_test);
test_accuracy = evaluation_metrics.classification_accuracy(Y_test, Y_test_pred);
fprintf('   测试集准确率: %.4f\n', test_accuracy);

% 详细评估指标
[precision, recall, f1] = evaluation_metrics.classification_metrics(Y_test, Y_test_pred);
fprintf('\n   各类别评估指标:\n');
fprintf('   类别\t精确率\t召回率\tF1分数\n');
for i = 1:n_classes
    fprintf('   %d\t%.4f\t%.4f\t%.4f\n', i, precision(i), recall(i), f1(i));
end

%% 4. 与传统ELM比较
fprintf('\n4. 与传统单层ELM比较...\n');

% 传统ELM (等效神经元总数)
total_neurons = sum(hidden_layers);
fprintf('   传统ELM (%d 个神经元)...\n', total_neurons);

elm_model = ML_ELM([total_neurons], ...
    'Activation', 'sigmoid', ...
    'C', 1, ...
    'BatchSize', 2000);

tic;
elm_model.train(X_train, Y_train);
elm_time = toc;

Y_test_pred_elm = elm_model.predict(X_test);
elm_accuracy = evaluation_metrics.classification_accuracy(Y_test, Y_test_pred_elm);

fprintf('   传统ELM结果:\n');
fprintf('     训练时间: %.2f秒\n', elm_time);
fprintf('     测试准确率: %.4f\n', elm_accuracy);
fprintf('   ML-ELM优势: +%.4f 准确率\n', test_accuracy - elm_accuracy);

%% 5. 不同网络结构比较
fprintf('\n5. 不同网络结构比较...\n');

network_architectures = {
    [100],           % 单层
    [150, 50],       % 两层
    [100, 80, 40],   % 三层
    [80, 60, 40, 20] % 四层
};

arch_names = {'单层[100]', '两层[150,50]', '三层[100,80,40]', '四层[80,60,40,20]'};
results = zeros(length(network_architectures), 3); % 时间, 训练准确率, 测试准确率

for i = 1:length(network_architectures)
    fprintf('   测试架构 %s...\n', arch_names{i});
    
    model = ML_ELM(network_architectures{i}, ...
        'Activation', 'sigmoid', ...
        'C', 1, ...
        'BatchSize', 2000);
    
    tic;
    model.train(X_train, Y_train);
    train_time = toc;
    
    Y_train_pred = model.predict(X_train);
    train_acc = evaluation_metrics.classification_accuracy(Y_train, Y_train_pred);
    
    Y_test_pred = model.predict(X_test);
    test_acc = evaluation_metrics.classification_accuracy(Y_test, Y_test_pred);
    
    results(i, :) = [train_time, train_acc, test_acc];
    
    fprintf('     时间: %.2fs, 训练准确率: %.4f, 测试准确率: %.4f\n', ...
        train_time, train_acc, test_acc);
end

% 显示比较结果
fprintf('\n   网络结构比较结果:\n');
fprintf('   架构\t\t\t时间(s)\t训练准确率\t测试准确率\n');
for i = 1:length(network_architectures)
    fprintf('   %s\t%.2f\t%.4f\t\t%.4f\n', ...
        arch_names{i}, results(i, 1), results(i, 2), results(i, 3));
end

%% 6. 大数据处理能力演示
fprintf('\n6. 大数据处理能力演示...\n');

% 生成超大规模数据
fprintf('   生成超大规模数据集 (100K样本)...\n');
X_huge = randn(100000, 50);
Y_huge = randi([1, 3], 100000, 1);
Y_huge_onehot = data_utils.onehot_encode(Y_huge);
X_huge_normalized = data_utils.normalize_data(X_huge, 'zscore');

% 测试不同批处理大小
batch_sizes = [500, 1000, 2000, 5000];
batch_results = zeros(length(batch_sizes), 2);

fprintf('   测试不同批处理大小:\n');
for i = 1:length(batch_sizes)
    fprintf('     批处理大小: %d...\n', batch_sizes(i));
    
    model = ML_ELM([100, 50], ...
        'Activation', 'sigmoid', ...
        'C', 1, ...
        'BatchSize', batch_sizes(i));
    
    tic;
    model.train(X_huge_normalized, Y_huge_onehot);
    batch_time = toc;
    
    batch_results(i, :) = [batch_sizes(i), batch_time];
    
    fprintf('       训练时间: %.2f秒\n', batch_time);
end

% 绘制批处理性能图
figure('Position', [200, 200, 800, 600]);
plot(batch_results(:, 1), batch_results(:, 2), 'ro-', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('批处理大小');
ylabel('训练时间 (秒)');
title('批处理大小对训练时间的影响');
grid on;

fprintf('\n=== 演示完成 ===\n');

5. 实际大数据应用示例

real_world_demo.m - 实际应用示例

%% 实际大数据应用示例
clear; clc; close all;

fprintf('=== ML-ELM实际大数据应用示例 ===\n\n');

%% 示例1: 图像分类 (使用预提取特征)
fprintf('示例1: 图像分类应用\n');

% 模拟图像特征数据 (假设已从CNN提取)
n_images = 30000;
n_features = 2048; % 典型CNN特征维度
n_categories = 10;

% 生成模拟图像特征
image_features = randn(n_images, n_features);
image_labels = randi([1, n_categories], n_images, 1);

% 数据预处理
X_images = data_utils.normalize_data(image_features, 'zscore');
Y_images = data_utils.onehot_encode(image_labels);

% 分割数据
[X_train_img, Y_train_img, X_test_img, Y_test_img] = ...
    data_utils.train_test_split(X_images, Y_images, 0.2);

% 创建ML-ELM模型
fprintf('训练图像分类ML-ELM...\n');
img_model = ML_ELM([512, 256, 128], ...
    'Activation', 'relu', ...
    'C', 0.1, ...
    'BatchSize', 1000);

img_model.train(X_train_img, Y_train_img);

% 评估
Y_pred_img = img_model.predict(X_test_img);
img_accuracy = evaluation_metrics.classification_accuracy(Y_test_img, Y_pred_img);
fprintf('图像分类准确率: %.4f\n', img_accuracy);

%% 示例2: 时间序列预测
fprintf('\n示例2: 时间序列预测\n');

% 生成时间序列数据
n_timepoints = 20000;
sequence_length = 100;
n_features_ts = 5;

% 创建多元时间序列
time_series_data = zeros(n_timepoints, n_features_ts);
for i = 1:n_features_ts
    time_series_data(:, i) = sin((1:n_timepoints)' * 0.1 * i) + ...
        0.5 * randn(n_timepoints, 1);
end

% 创建滑动窗口特征
X_ts = [];
Y_ts = [];
window_size = 10;
prediction_horizon = 1;

for i = window_size:n_timepoints-prediction_horizon
    window_features = time_series_data(i-window_size+1:i, :);
    target = time_series_data(i+prediction_horizon, 1); % 预测第一个特征
    
    X_ts = [X_ts; window_features(:)'];
    Y_ts = [Y_ts; target];
end

% 数据预处理
X_ts_normalized = data_utils.normalize_data(X_ts, 'zscore');
Y_ts_normalized = data_utils.normalize_data(Y_ts, 'zscore');

% 分割数据
[X_train_ts, Y_train_ts, X_test_ts, Y_test_ts] = ...
    data_utils.train_test_split(X_ts_normalized, Y_ts_normalized, 0.2);

% 创建回归模型
fprintf('训练时间序列预测ML-ELM...\n');
ts_model = ML_ELM([50, 25], ...
    'Activation', 'tanh', ...
    'C', 0.01, ...
    'BatchSize', 500);

ts_model.train(X_train_ts, Y_train_ts);

% 评估回归性能
Y_pred_ts = ts_model.predict(X_test_ts);
mse_ts = evaluation_metrics.mean_squared_error(Y_test_ts, Y_pred_ts);
r2_ts = evaluation_metrics.r_squared(Y_test_ts, Y_pred_ts);

fprintf('时间序列预测 - MSE: %.6f, R²: %.4f\n', mse_ts, r2_ts);

%% 示例3: 推荐系统
fprintf('\n示例3: 推荐系统应用\n');

% 模拟用户-物品交互数据
n_users = 10000;
n_items = 5000;
n_interactions = 200000;

% 生成稀疏交互矩阵
user_ids = randi([1, n_users], n_interactions, 1);
item_ids = randi([1, n_items], n_interactions, 1);
ratings = randi([1, 5], n_interactions, 1);

% 创建特征矩阵 (用户特征 + 物品特征)
user_features = randn(n_users, 20);
item_features = randn(n_items, 20);

% 构建训练数据
X_recommend = [];
Y_recommend = [];

for i = 1:n_interactions
    user_feat = user_features(user_ids(i), :);
    item_feat = item_features(item_ids(i), :);
    
    X_recommend = [X_recommend; [user_feat, item_feat]];
    Y_recommend = [Y_recommend; ratings(i)];
end

% 数据预处理和分割
X_rec_normalized = data_utils.normalize_data(X_recommend, 'minmax');
[X_train_rec, Y_train_rec, X_test_rec, Y_test_rec] = ...
    data_utils.train_test_split(X_rec_normalized, Y_recommend, 0.2);

% 创建推荐模型
fprintf('训练推荐系统ML-ELM...\n');
rec_model = ML_ELM([64, 32], ...
    'Activation', 'sigmoid', ...
    'C', 0.1, ...
    'BatchSize', 2000);

rec_model.train(X_train_rec, Y_train_rec);

% 评估推荐性能
Y_pred_rec = rec_model.predict(X_test_rec);
mse_rec = evaluation_metrics.mean_squared_error(Y_test_rec, Y_pred_rec);
fprintf('推荐系统预测 - MSE: %.6f\n', mse_rec);

fprintf('\n=== 所有示例完成 ===\n');

参考代码 多隐含层极限学习机，适合对大数据进行处理 www.youwenfan.com/contentcnk/63624.html

6. 关键特性总结

6.1 大数据处理优势

• 批处理机制: 支持大规模数据分块处理
• 内存优化: 避免一次性加载所有数据
• 并行潜力: 易于扩展为并行计算

6.2 网络结构优势

• 深度特征提取: 多层级联的非线性变换
• 自适应结构: 可灵活配置各层神经元数量
• 快速训练: 保持ELM的快速训练特性

6.3 实际应用价值

1. 图像识别: 处理大规模图像特征
2. 时间序列: 长期依赖关系建模
3. 推荐系统: 用户行为模式学习
4. 工业检测: 大规模质量监控数据