【GRNN预测】基于鲸鱼算法优化广义回归神经网络实现数据预测Matlab代码

1 简介

​为实现精准施肥"减施增效"的数字化农业施肥技术,本文基于并运用了鲸鱼算法,对广义回归神经网络(GRNN)进行了结合与改进,并构建作物广义回归神经网络(GRNN)结合遗传算法的预测施肥量模型.通过采集得到的数据样本会被用来输入MATLAB进行仿真和实验验证.仿真和实验结果表明,改进后的基于鲸鱼算法的GRNN神经网络模型比BP神经网络具有更少的输入参数,能更好地反映施肥量与诸多影响因素之间的关系,具有实用价值.且基于鲸鱼算法改进的GRNN神经网络算法模型人为设定量更少,更为客观,预测值与实际值之间的误差更小,预测结果更加准确.

2 部分代码

%_________________________________________________________________________%

% Whale Optimization Algorithm (WOA) source codes demo 1.0               %

% The Whale Optimization Algorithm

function [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

% initialize position vector and score for the leader

Leader_pos=zeros(1,dim);

Leader_score=inf; %change this to -inf for maximization problems

%Initialize the positions of search agents

% Positions=initialization(SearchAgents_no,dim,ub,lb);

Positions=ceil(rand(SearchAgents_no,dim).*(ub-lb)+lb);

Convergence_curve=zeros(1,Max_iter);

t=0;% Loop counter

% Main loop

while t<Max_iter

   for i=1:size(Positions,1)

       

       % Return back the search agents that go beyond the boundaries of the search space

       Flag4ub=Positions(i,:)>ub;

       Flag4lb=Positions(i,:)<lb;

       Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;

       

       % Calculate objective function for each search agent

       fitness=fobj(Positions(i,:));

       

       % Update the leader

       if fitness<Leader_score % Change this to > for maximization problem

           Leader_score=fitness; % Update alpha

           Leader_pos=Positions(i,:);

       end

       

   end

   

   a=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 paper

       C=2*r2;      % Eq. (2.4) in the paper

       

       

       b=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)>=1

                   rand_leader_index = floor(SearchAgents_no*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)<1

                   D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1)

                   Positions(i,j)=Leader_pos(j)-A*D_Leader;      % Eq. (2.2)

               end

               

           elseif p>=0.5

             

               distance2Leader=abs(Leader_pos(j)-Positions(i,j));

               % Eq. (2.5)

               Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Leader_pos(j);

               

           end

           

       end

   end

   t=t+1;

   Convergence_curve(t)=Leader_score;

%     [t Leader_score]

end

3 仿真结果

【GRNN预测】基于鲸鱼算法优化广义回归神经网络实现数据预测Matlab代码

4 参考文献

[1]倪贤达, 杨得航, 左桐,等. 基于遗传算法改进GRNN神经网络的施肥量预测研究[J].  2020.

部分理论引用网络文献,若有侵权联系博主删除。

5 MATLAB代码与数据下载地址

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