【BiLSTM分类】基于matlab麻雀算法优化双向长短期记忆神经网络数据分类预测【含Matlab源码 3004期】.zip

%% 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 tic % restoredefaultpath %% 读取数据 res = xlsread('数据集.xlsx'); %% 划分训练集和测试集% P_train = res(1: 250, 1: 12)'; T_train = res(1: 250, 13)'; M = size(P_train, 2); P_test = res(251: end, 1: 12)'; T_test = res(251: end, 13)'; N = size(P_test, 2); num_dim = size(P_train, 1); % 特征维度 num_class = length(unique(res(:, end))); % 类别数（Excel最后一列放类别） % 类别数（Excel最后一列放类别） %% 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 = categorical(T_train)'; t_test = categorical(T_test )'; %% 格式转换 for i = 1 : M p_train{i, 1} = P_train(:, i); end for i = 1 : N p_test{i, 1} = P_test(:, i); end %% 优化算法参数设置 pop = 6; % 数量 Max_iter =20; % 最大迭代次数 dim = 3; % 优化参数个数 lb = [1e-3, 10, 1e-4]; % 参数取值下界(学习率，隐藏层节点，正则化系数) ub = [1e-2, 30, 1e-1]; % 参数取值上界(学习率，隐藏层节点，正则化系数) fitness = @(x)fical(x,p_train,t_train,T_train,num_dim,num_class); [Best_pos,V,curve]=SSA(pop,Max_iter,lb ,ub,dim,fitness); %% 记录最佳参数 best_lr = Best_pos(1, 1); best_hd = Best_pos(1, 2); best_l2 = Best_pos(1, 3); %% 建立模型 % ---------------------- 修改模型结构时需对应修改fical.m中的模型结构 -------------------------- layers = [ sequenceInputLayer(num_dim) % 输入层 bilstmLayer(best_hd, 'OutputMode', 'last') % BILSTM层 reluLayer % Relu激活层 fullyConnectedLayer(num_class) % 全连接层 softmaxLayer % 损失函数层 classificationLayer]; % 分类层 %% 参数设置 % ---------------------- 修改模型参数时需对应修改fical.m中的模型参数 -------------------------- options = trainingOptions('adam', ... % Adam 梯度下降算法 'MaxEpochs', 500, ... % 最大训练次数 500 'InitialLearnRate', best_lr, ... % 初始学习率 best_lr 'LearnRateSchedule', 'piecewise', ... % 学习率下降 'LearnRateDropFactor', 0.5, ... % 学习率下降因子 0.1 'LearnRateDropPeriod', 400, ... % 经过 400 次训练后 学习率为 best_lr * 0.5 'Shuffle', 'every-epoch', ... % 每次训练打乱数据集 'ValidationPatience', Inf, ... % 关闭验证 'L2Regularization', best_l2, ... % 正则化参数 'Plots', 'training-progress', ... % 画出曲线 'Verbose', false); %% 训练模型 net = trainNetwork(p_train, t_train, layers, options); %% 仿真验证 t_sim1 = predict(net, p_train); t_sim2 = predict(net, p_test ); %% 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 ; toc %% 数据排序 % [T_train, index_1] = SSArt(T_train); % [T_test , index_2] = SSArt(T_test ); % % T_sim1 = T_sim1(index_1); % T_sim2 = T_sim2(index_2); %% 优化曲线 figure plot(curve, 'linewidth',1.5); title('SSA-BiLSTM') xlabel('The number of iterations') ylabel('Fitness') grid on; %% 绘图 figure plot(1: M, T_train, 'r*', 1: M, T_sim1, 'bo', 'LineWidth', 1) legend('真实值', 'SSA-BiLSTM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, 'bo', 'LineWidth', 1) legend('真实值', 'SSA-BiLSTM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 %% 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';