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分类预测 | MATLAB实现BWO-TCN-Attention数据分类预测

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_pos=zeros(1,dim);
Best_Cost=inf; %change this to -inf for maximization problems%Initialize the positions of search agents
Positions=initialization(pop,dim,ub,lb);curve=zeros(1,Max_iter);t=0;% Loop counter% Main loop
while t<Max_iterfor i=1:size(Positions,1)% Return back the search agents that go beyond the boundaries of the search spaceFlag4ub=Positions(i,:)>ub;Flag4lb=Positions(i,:)<lb;Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;% Calculate objective function for each search agentfitness=fobj(Positions(i,:));% Update the leaderif fitness<Best_Cost % Change this to > for maximization problemBest_Cost=fitness; % Update alphaBest_pos=Positions(i,:);endenda=2-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=-1+t*((-1)/Max_iter);% Update the Position of search agents for i=1:size(Positions,1)r1=rand(); % r1 is a random number in [0,1]r2=rand(); % r2 is a random number in [0,1]A=2*a*r1-a;  % Eq. (2.3) in the paperC=2*r2;      % Eq. (2.4) in the paperb=1;               %  parameters in Eq. (2.5)l=(a2-1)*rand+1;   %  parameters in Eq. (2.5)p = rand();        % p in Eq. (2.6)for j=1:size(Positions,2)if p<0.5   if abs(A)>=1rand_leader_index = floor(pop*rand()+1);X_rand = Positions(rand_leader_index, :);D_X_rand=abs(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_Leader=abs(C*Best_pos(j)-Positions(i,j)); % Eq. (2.1)Positions(i,j)=Best_pos(j)-A*D_Leader;      % Eq. (2.2)endelseif p>=0.5distance2Leader=abs(Best_pos(j)-Positions(i,j));% Eq. (2.5)Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Best_pos(j);endendendt=t+1;curve(t)=Best_Cost;[t Best_Cost]
end

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229