【BP预测】基于人工蜂群算法优化BP神经网络实现数据预测附matlab代码

1 简介

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。

2 部分代码

function [bestsol,yy] = ABC(prob,lb,ub,Np,T,limit)​%% Starting of ABCf = NaN(Np,1);                      % Vector to store the objective function value of the population membersfit = NaN(Np,1);                    % Vector to store the fitness function value of the population memberstrial = NaN(Np,1);                  % Initialization of the trial vector​D = length(lb);                     % Determining the number of decision variables in the problem​P = repmat(lb,Np,1) + repmat((ub-lb),Np,1).*rand(Np,D);   % Generation of the initial population​for p = 1:Np    f(p) = prob(P(p,:));            % Evaluating the objective function value    fit(p) = CalFit(f(p));          % Evaluating the fitness function valueend​[bestobj, ind] = min(f);            % Determine and memorize the best objective valuebestsol = P(ind,:);                 % Determine and memorize the best solutionbestobj1=1000;for t = 1:T        %% Employed Bee Phase    for i = 1:Np        [trial,P,fit,f] = GenNewSol(prob, lb, ub, Np, i, P, fit, trial, f, D);    end        %% Onlooker Bee Phase    % as per the code of the inventors available at https://abc.erciyes.edu.tr/    % prob=(0.9.*Fitness./max(Fitness))+0.1;    % MATLAB Code of the ABC algorithm version 2 has been released (14.12.2009) (more optimized coding)        probability = 0.9 * (fit/max(fit)) + 0.1;        m = 0; n = 1;        while(m < Np)        if(rand < probability(n))            [trial,P,fit,f] = GenNewSol(prob, lb, ub, Np, n, P, fit, trial, f, D);            m = m + 1;        end        n = mod(n,Np) + 1;    end        [bestobj,ind] = min([f;bestobj]);    CombinedSol = [P;bestsol];    bestsol = CombinedSol(ind,:);    if bestobj<bestobj1        yy(t)=bestobj;    else          yy(t)=bestobj1;    end             %% Scout Bee Phase    [val,ind] = max(trial);        if (val > limit)        trial(ind) = 0;                     % Reset the trial value to zero        P(ind,:) = lb + (ub-lb).*rand(1,D); % Generate a random solution        f(ind) = prob(P(ind,:));            % Determine the objective function value of the newly generated solution        fit(ind) = CalFit(f(ind));          % Determine the fitness function value of the newly generated solution    endend​[bestfitness,ind] = min([f;bestobj]);CombinedSol = [P;bestsol];bestsol = CombinedSol(ind,:);

3 仿真结果

​4 参考文献

[1]苏彩红, 向娜, 陈广义,等. 基于人工蜂群算法与BP神经网络的水质评价模型[J]. 环境工程学报, 2012.

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