1688网站如何运营,农商网站建设个人总结,温州建网站公司哪家好,win2003搭建php网站分类预测 | MATLAB实现BWO-TCN-Attention数据分类预测 目录 分类预测 | MATLAB实现BWO-TCN-Attention数据分类预测分类效果基本描述程序设计参考资料 分类效果 基本描述 1.BWO-TCN-Attention数据分类预测程序#xff1b; 2.无Attention适用于MATLAB 2022b版及以上版本#xf…分类预测 | MATLAB实现BWO-TCN-Attention数据分类预测 目录 分类预测 | MATLAB实现BWO-TCN-Attention数据分类预测分类效果基本描述程序设计参考资料 分类效果 基本描述 1.BWO-TCN-Attention数据分类预测程序 2.无Attention适用于MATLAB 2022b版及以上版本融合Attention要求Matlab2023版以上 3.基于白鲸优化算法BWO、时间卷积神经网络TCN融合注意力机制的数据分类预测程序 程序语言为matlab程序可出分类效果图迭代优化图混淆矩阵图精确度、召回率、精确率、F1分数等评价指标。 4.算法优化学习率、卷积核大小、神经元个数这3个关键参数以测试集精度最高为目标函数。 5.适用领域 适用于各种数据分类场景如滚动轴承故障、变压器油气故障、电力系统输电线路故障区域、绝缘子、配网、电能质量扰动等领域的识别、诊断和分类。 使用便捷 直接使用EXCEL表格导入数据无需大幅修改程序。内部有详细注释易于理解。 程序设计
完整程序和数据获取方式私信博主回复MATLAB实现BWO-TCN-Attention数据分类预测
% The Whale Optimization Algorithm
function [Best_Cost,Best_pos,curve]WOA(pop,Max_iter,lb,ub,dim,fobj)% initialize position vector and score for the leader
Best_poszeros(1,dim);
Best_Costinf; %change this to -inf for maximization problems%Initialize the positions of search agents
Positionsinitialization(pop,dim,ub,lb);curvezeros(1,Max_iter);t0;% Loop counter% Main loop
while tMax_iterfor i1:size(Positions,1)% Return back the search agents that go beyond the boundaries of the search spaceFlag4ubPositions(i,:)ub;Flag4lbPositions(i,:)lb;Positions(i,:)(Positions(i,:).*(~(Flag4ubFlag4lb)))ub.*Flag4ublb.*Flag4lb;% Calculate objective function for each search agentfitnessfobj(Positions(i,:));% Update the leaderif fitnessBest_Cost % Change this to for maximization problemBest_Costfitness; % Update alphaBest_posPositions(i,:);endenda2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)a2-1t*((-1)/Max_iter);% Update the Position of search agents for i1:size(Positions,1)r1rand(); % r1 is a random number in [0,1]r2rand(); % r2 is a random number in [0,1]A2*a*r1-a; % Eq. (2.3) in the paperC2*r2; % Eq. (2.4) in the paperb1; % parameters in Eq. (2.5)l(a2-1)*rand1; % parameters in Eq. (2.5)p rand(); % p in Eq. (2.6)for j1:size(Positions,2)if p0.5 if abs(A)1rand_leader_index floor(pop*rand()1);X_rand Positions(rand_leader_index, :);D_X_randabs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)Positions(i,j)X_rand(j)-A*D_X_rand; % Eq. (2.8)elseif abs(A)1D_Leaderabs(C*Best_pos(j)-Positions(i,j)); % Eq. (2.1)Positions(i,j)Best_pos(j)-A*D_Leader; % Eq. (2.2)endelseif p0.5distance2Leaderabs(Best_pos(j)-Positions(i,j));% Eq. (2.5)Positions(i,j)distance2Leader*exp(b.*l).*cos(l.*2*pi)Best_pos(j);endendendtt1;curve(t)Best_Cost;[t Best_Cost]
end参考资料 [1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm1001.2014.3001.5502 [2] https://blog.csdn.net/kjm13182345320/article/details/128690229
