24新算法CPO-BP冠豪猪算法CPO优化BP神经网络分类预测，CPO-BP分类预测，多特征输入模型 多特征输入单输出的

%% 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'); %% 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); %% 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 = 100; % 训练次数 net.trainParam.goal = 1e-4; % 目标误差 net.trainParam.lr = 0.01; % 学习率 net.trainParam.showWindow = 0; % 关闭窗口 %% 麻雀参数设置 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 = 10; % 数量 Max_iteration = 20; % 最大迭代次数 %% 优化算法 [ Best_score,Best_pos, curve] = CPO(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); %% 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 ; %% 优化曲线 figure plot(curve, 'linewidth',1.5); title('CPO-BP') 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', 'CPO-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, 'bo', 'LineWidth', 1) legend('真实值', 'CPO-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 %% 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';