【负荷预测】布谷鸟(CS)算法优化BP神经网络的负荷及天气预测附Matlab代码

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智能优化算法  神经网络预测 雷达通信  无线传感器

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⛄ 内容介绍

锂电池健康状态(SOH)的预测是电动汽车锂电池管理系统的最重要的关键技术之一;传统的误差逆向传播(BP)神经网络容易使权值和阈值陷入局部最优,从而导致预测结果不精确;结合布谷鸟搜索算法(CS)的全局优化能力,提出一种基于CS算法优化BP神经网络的锂电池SOH预测方法,该方法的核心在于优化BP神经网络的初始权值和阈值,从而减小算法对初始值的依赖。

⛄ 部分代码

function [bestnest,fmin]=cuckoo_search(n,inputnum,hiddennum,outputnum,net,inputn,outputn)                                   %n为鸟巢数目

if nargin<1, % nargin是用来判断输入变量个数的函数

% Number of nests (or different solutions)

n=25;

end

% Discovery rate of alien eggs/solutions

pa=0.25;

%% Change this if you want to get better results

% Tolerance

Tol=1.0e-5;                                                                 %为最大迭代次数限制

%% Simple bounds of the search domain

% Lower bounds

nd=22; 

Lb=-5*ones(1,nd); 

% Upper bounds

Ub=5*ones(1,nd);

                                                                            %随机产生初始解

% Random initial solutions

for i=1:n,      

nest(i,:)=Lb+(Ub-Lb).*rand(size(Lb));

end

                                                                            %得到当前的最优解

    s=nest(j,:);

    % This is a simple way of implementing Levy flights

    % For standard random walks, use step=1;

    %% Levy flights by Mantegna's algorithm

    u=randn(size(s))*sigma;

    v=randn(size(s));

    step=u./abs(v).^(1/beta);

  

    % In the next equation, the difference factor (s-best) means that 

    % when the solution is the best solution, it remains unchanged.     

    stepsize=0.01*step.*(s-best);

    % Here the factor 0.01 comes from the fact that L/100 should the typical

    % step size of walks/flights where L is the typical lenghtscale; 

    % otherwise, Levy flights may become too aggresive/efficient, 

    % which makes new solutions (even) jump out side of the design domain 

    % (and thus wasting evaluations).

    % Now the actual random walks or flights

    s=s+stepsize.*randn(size(s));

   % Apply simple bounds/limits

   nest(j,:)=simplebounds(s,Lb,Ub);

end

%% Find the current best nest

function [fmin,best,nest,fitness]=get_best_nest(nest,newnest,fitness,inputnum,hiddennum,outputnum,net,inputn,outputn)

% Evaluating all new solutions

for j=1:size(nest,1),

    fnew=fobj(newnest(j,:),inputnum,hiddennum,outputnum,net,inputn,outputn);

    if fnew<=fitness(j),

       fitness(j)=fnew;

       nest(j,:)=newnest(j,:);

    end

end

% Find the current best

[fmin,K]=min(fitness) ;

best=nest(K,:);

%% Replace some nests by constructing new solutions/nests

function new_nest=empty_nests(nest,Lb,Ub,pa)

% A fraction of worse nests are discovered with a probability pa

n=size(nest,1);

% Discovered or not -- a status vector

K=rand(size(nest))>pa;

% In the real world, if a cuckoo's egg is very similar to a host's eggs, then 

% this cuckoo's egg is less likely to be discovered, thus the fitness should 

% be related to the difference in solutions.  Therefore, it is a good idea 

% to do a random walk in a biased way with some random step sizes.  

%% New solution by biased/selective random walks

stepsize=rand*(nest(randperm(n),:)-nest(randperm(n),:));

new_nest=nest+stepsize.*K;

% Application of simple constraints

function s=simplebounds(s,Lb,Ub)

  % Apply the lower bound

  ns_tmp=s;

  I=ns_tmp

  ns_tmp(I)=Lb(I);

  

  % Apply the upper bounds 

  J=ns_tmp>Ub;

  ns_tmp(J)=Ub(J);

  % Update this new move 

  s=ns_tmp;

⛄ 运行结果

⛄ 参考文献

[1]孙晨, 李阳, 李晓戈, et al. 基于布谷鸟算法优化BP神经网络模型的股价预测[J]. 计算机应用与软件, 2016, 33(2):4.

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