黄州网站建设,服务器租用租赁,网站开发设计资料,政协信息化网站建设的请示分类预测 | Matlab实现WOA-BiLSTM鲸鱼算法优化双向长短期记忆神经网络的数据多输入分类预测 目录 分类预测 | Matlab实现WOA-BiLSTM鲸鱼算法优化双向长短期记忆神经网络的数据多输入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.Matlab实现WOA-BiLSTM鲸鱼算法…分类预测 | Matlab实现WOA-BiLSTM鲸鱼算法优化双向长短期记忆神经网络的数据多输入分类预测 目录 分类预测 | Matlab实现WOA-BiLSTM鲸鱼算法优化双向长短期记忆神经网络的数据多输入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.Matlab实现WOA-BiLSTM鲸鱼算法优化双向长短期记忆神经网络的数据多输入分类预测运行环境Matlab2020b及以上 2.基于鲸鱼算法(WOA)优化双向长短期记忆网络(BiLSTM)分类预测优化参数为学习率隐含层节点正则化参数 3.多特征输入单输出的二分类及多分类模型。程序内注释详细直接替换数据就可以用 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 4.data为数据集输入12个特征分四类main为主程序其余为函数文件无需运行可在下载区获取数据和程序内容。 程序设计
完整程序和数据获取方式1私信博主同等价值程序兑换完整程序和数据下载方式2(资源处直接下载)Matlab实现WOA-BiLSTM鲸鱼算法优化双向长短期记忆神经网络的数据多输入分类预测
% 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 problemscurvezeros(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
