基于RIME-BP霜冰算法优化BP神经网络多特征分类预测（matlab完整源码数据）

%% 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 %% 读取数据 res = xlsread('data.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 = 15; % 隐藏层节点数 outputnum = size(t_train, 1); % 输出层节点数 %% 构建网络 net = newff(p_train, t_train, hiddennum); %% 设置训练参数 net.trainParam.epochs = 50; % 训练次数 net.trainParam.goal = 1e-3; % 目标误差 net.trainParam.lr = 0.01; % 学习率 net.trainParam.showWindow = 0; % 关闭窗口 %% 参数设置 fun = @getObjValue; % 目标函数 dim = inputnum * hiddennum + hiddennum * outputnum + ... hiddennum + outputnum; % 优化参数个数 lb = -1 * ones(1, dim); % 优化参数目标下限 ub = 1 * ones(1, dim); % 优化参数目标上限 pop = 20; % 数量 Max_iteration = 20; % 最大迭代次数 %% 优化算法 [Best_score,Best_pos,curve] = RIME(pop, Max_iteration, lb, ub, dim, fun); %% 把最优初始阀值权值赋予网络预测 w1 = Best_pos(1 : inputnum * hiddennum); B1 = Best_pos(inputnum * hiddennum + 1 : inputnum * hiddennum + hiddennum); w2 = Best_pos(inputnum * hiddennum + hiddennum + 1 : inputnum * hiddennum + hiddennum + hiddennum*outputnum); B2 = Best_pos(inputnum * hiddennum + hiddennum + hiddennum * outputnum + 1 : ... inputnum * hiddennum + hiddennum + hiddennum * outputnum + outputnum); 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'; %% 网络训练 net.trainParam.showWindow = 1; % 打开窗口 net = train(net, p_train, t_train); %% BP网络预测 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); %% 性能评价 error1 = sum((T_sim1 == T_train)) / M * 100 ; error2 = sum((T_sim2 == T_test )) / N * 100 ; %% 绘图 figure() plot(T_sim1,'o','Color',[255 0 255]./255,'linewidth',1) hold on plot(T_train,'-+','Color',[0 0 0]./255,'linewidth',1) legend('预测值','真实值') 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(T_sim2,'o','Color',[250 180 60]./255,'linewidth',2) hold on plot(T_test,'-+','Color',[0 0 0]./255,'linewidth',2) legend('预测值','真实值') 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 %% 优化曲线 figure plot(curve, 'linewidth',1.5); title('RIME') xlabel('The number of iterations') ylabel('Fitness') grid on; %% 混淆矩阵 if flag_conusion == 1 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'; end