基于PSO-BP的数据分类预测（附带免费的Matlab完整代码）

%% 面包多：机器学习和优化算法 %% 代码列表：https://docs.qq.com/sheet/DSXFYQ2VpRXRLSFZn?tab=000001&_bid=1&client=drive_file %% 清空环境变量 warning off % 关闭报警信息 close all % 关闭开启的图窗 clear % 清空变量 clc % 清空命令行 %% 导入数据 res = xlsread('数据集.xlsx'); %% 分析数据 num_class = length(unique(res(:, end))); % 类别数（Excel最后一列放类别） num_res = size(res, 1); % 样本数（每一行，是一个样本） num_size = 0.7; % 训练集占数据集的比例 res = res(randperm(num_res), :); % 打乱数据集（不打乱数据时，注释该行） flag_conusion = 1; % 标志位为1，打开混淆矩阵（要求2018版本及以上） %% 设置变量存储数据 P_train = []; P_test = []; T_train = []; T_test = []; %% 划分数据集 for i = 1 : num_class mid_res = res((res(:, end) == i), :); % 循环取出不同类别的样本 mid_size = size(mid_res, 1); % 得到不同类别样本个数 mid_tiran = round(num_size * mid_size); % 得到该类别的训练样本个数 P_train = [P_train; mid_res(1: mid_tiran, 1: end - 1)]; % 训练集输入 T_train = [T_train; mid_res(1: mid_tiran, end)]; % 训练集输出 P_test = [P_test; mid_res(mid_tiran + 1: end, 1: end - 1)]; % 测试集输入 T_test = [T_test; mid_res(mid_tiran + 1: end, end)]; % 测试集输出 end %% 数据转置 P_train = P_train'; P_test = P_test'; T_train = T_train'; T_test = T_test'; %% 得到训练集和测试样本个数 M = size(P_train, 2); N = size(P_test , 2); %% 数据归一化 [p_train, ps_input] = mapminmax(P_train, 0, 1); p_test = mapminmax('apply', P_test, ps_input); t_train = ind2vec(T_train); t_test = ind2vec(T_test ); %% 节点个数 inputnum = size(p_train, 1); % 输入层节点数 hiddennum = 6; % 隐藏层节点数 outputnum = size(t_train, 1); % 输出层节点数 %% 建立网络 net = newff(p_train, t_train, hiddennum); %% 设置训练参数 net.trainParam.epochs = 1000; % 训练次数 net.trainParam.goal = 1e-6; % 目标误差 net.trainParam.lr = 0.01; % 学习率 net.trainParam.showWindow = 0; % 关闭窗口 %% 参数初始化 c1 = 4.494; % 学习因子 c2 = 4.494; % 学习因子 maxgen = 30; % 种群更新次数 sizepop = 5; % 种群规模 Vmax = 1.0; % 最大速度 Vmin = -1.0; % 最小速度 popmax = 2.0; % 最大边界 popmin = -2.0; % 最小边界 %% 节点总数 numsum = inputnum * hiddennum + hiddennum + hiddennum * outputnum + outputnum; for i = 1 : sizepop pop(i, :) = rands(1, numsum); % 初始化种群 V(i, :) = rands(1, numsum); % 初始化速度 fitness(i) = fun(pop(i, :), hiddennum, net, p_train, t_train); end %% 个体极值和群体极值 [fitnesszbest, bestindex] = min(fitness); zbest = pop(bestindex, :); % 全局最佳 gbest = pop; % 个体最佳 fitnessgbest = fitness; % 个体最佳适应度值 BestFit = fitnesszbest; % 全局最佳适应度值 %% 迭代寻优 for i = 1 : maxgen for j = 1 : sizepop % 速度更新 V(j, :) = V(j, :) + c1 * rand * (gbest(j, :) - pop(j, :)) + c2 * rand * (zbest - pop(j, :)); V(j, (V(j, :) > Vmax)) = Vmax; V(j, (V(j, :) < Vmin)) = Vmin; % 种群更新 pop(j, :) = pop(j, :) + 0.2 * V(j, :); pop(j, (pop(j, :) > popmax)) = popmax; pop(j, (pop(j, :) < popmin)) = popmin; % 自适应变异 pos = unidrnd(numsum); if rand > 0.95 pop(j, pos) = rands(1, 1); end % 适应度值 fitness(j) = fun(pop(j, :), hiddennum, net, p_train, t_train); end for j = 1 : sizepop % 个体最优更新 if fitness(j) < fitnessgbest(j) gbest(j, :) = pop(j, :); fitnessgbest(j) = fitness(j); end % 群体最优更新 if fitness(j) < fitnesszbest zbest = pop(j, :); fitnesszbest = fitness(j); end end BestFit = [BestFit, fitnesszbest]; end %% 提取最优初始权值和阈值 w1 = zbest(1 : inputnum * hiddennum); B1 = zbest(inputnum * hiddennum + 1 : inputnum * hiddennum + hiddennum); w2 = zbest(inputnum * hiddennum + hiddennum + 1 : inputnum * hiddennum ... + hiddennum + hiddennum * outputnum); B2 = zbest(inputnum * hiddennum + hiddennum + hiddennum * outputnum + 1 : ... inputnum * hiddennum + hiddennum + hiddennum * outputnum + outputnum); %% 网络赋值 net.Iw{1, 1} = reshape(w1, hiddennum, inputnum ); net.Lw{2, 1} = reshape(w2, outputnum, hiddennum); net.b{1} = reshape(B1, hiddennum, 1); net.b{2} = B2'; %% 打开训练窗口 net.trainParam.showWindow = 1; % 打开窗口 %% 网络训练 net = train(net, p_train, t_train); %% 仿真预测 t_sim1 = sim(net, p_train); t_sim2 = sim(net, p_test ); %% 数据反归一化 T_sim1 = vec2ind(t_sim1); T_sim2 = vec2ind(t_sim2); %% 数据排序 [T_train, index_1] = sort(T_train); [T_test , index_2] = sort(T_test ); T_sim1 = T_sim1(index_1); T_sim2 = T_sim2(index_2); %% 性能评价 error1 = sum((T_sim1 == T_train)) / M * 100 ; error2 = sum((T_sim2 == T_test )) / N * 100 ; %% 绘图 figure plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1) legend('真实值', 'PSO-BP预测值') xlabel('预测样本') ylabel('预测结果') string = {'训练集预测结果对比'; ['准确率=' num2str(error1) '%']}; title(string) xlim([1, M]) grid figure plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1) legend('真实值', 'PSO-BP预测值') xlabel('预测样本') ylabel('预测结果') string = {'测试集预测结果对比'; ['准确率=' num2str(error2) '%']}; title(string) xlim([1, N]) grid %% 误差曲线迭代图 figure plot(1: length(BestFit), BestFit, 'LineWidth', 1.5); xlabel('粒子群迭代次数'); ylabel('适应度值'); xlim([1, length(BestFit)]) string = {'模型迭代误差变化'}; title(string) grid on %% 混淆矩阵 figure cm = confusionchart(T_train, T_sim1); cm.Title = 'Confusion Matrix for Train Data'; cm.ColumnSummary = 'column-normalized'; cm.RowSummary = 'row-normalized'; figure cm = confusionchart(T_test, T_sim2); cm.Title = 'Confusion Matrix for Test Data'; cm.ColumnSummary = 'column-normalized'; cm.RowSummary = 'row-normalized'; %% 面包多：机器学习和优化算法 %% 代码列表：https://docs.qq.com/sheet/DSXFYQ2VpRXRLSFZn?tab=000001&_bid=1&client=drive_file