【BP预测】基于斑点鬣狗算法优化BP神经网络实现数据预测附matlab代码

1 简介

为了寻求有效控制和改善环境质量的相应措施,选用了英国伦敦Bloomsbury监测站的PM10小时平均浓度监测资料,采用基于斑点鬣狗算法的BP神经网络模型,预测PM1024 h内的小时平均浓度.结果表明:采用BP神经网络法对大气污染物浓度进行预测,预测相对误差在2%-48%之间,且绝大部分在2%-17%之间,预测精度较高,泛化能力较好,为大气污染物浓度预测提供了一种全新的思路和方法.

2 部分代码

function [Best_hyena_score,Best_hyena_pos,Convergence_curve]=sho(N,Max_iterations,lowerbound,upperbound,dimension,fitness)
hyena_pos=init(N,dimension,upperbound,lowerbound);
Convergence_curve=zeros(1,Max_iterations);
Iteration=1;
while Iteration<Max_iterations
    for i=1:size(hyena_pos,1)
        H_ub=hyena_pos(i,:)>upperbound;
        H_lb=hyena_pos(i,:)<lowerbound;
        hyena_pos(i,:)=(hyena_pos(i,:).*(~(H_ub+H_lb)))+upperbound.*H_ub+lowerbound.*H_lb;  
        hyena_fitness(1,i)=fitness(hyena_pos(i,:));  
    end
    if Iteration==1
        [fitness_sorted FS]=sort(hyena_fitness);
        sorted_population=hyena_pos(FS,:);
        best_hyenas=sorted_population;
        best_hyena_fitness=fitness_sorted;
    else
        double_population=[pre_population;best_hyenas];
        double_fitness=[pre_fitness best_hyena_fitness];
        [double_fitness_sorted FS]=sort(double_fitness);
        double_sorted_population=double_population(FS,:);
        fitness_sorted=double_fitness_sorted(1:N);
        sorted_population=double_sorted_population(1:N,:);
        best_hyenas=sorted_population;
        best_hyena_fitness=fitness_sorted;
    end
    NOH=noh(best_hyena_fitness);
    Best_hyena_score=fitness_sorted(1);
    Best_hyena_pos=sorted_population(1,:);
    pre_population=hyena_pos;
    pre_fitness=hyena_fitness;
    a=5-Iteration*((5)/Max_iterations);
    HYE=0;
    CV=0;
    for i=1:size(hyena_pos,1)
        for j=1:size(hyena_pos,2)
             for k=1:NOH
            HYE=0;
             r1=rand();
             r2=rand(); 
            Var1=2*a*r1-a; 
            Var2=2*r2; 
            distance_to_hyena=abs(Var2*sorted_population(k)-hyena_pos(i,j));
            HYE=sorted_population(k)-Var1*distance_to_hyena;
            CV=CV+HYE;        
            distance_to_hyena=0;
             end
        hyena_pos(i,j)=(CV/(NOH+1));
        CV=0;
        end
    end
    Convergence_curve(Iteration)=Best_hyena_score;
     Iteration=Iteration+1; 
end

3 仿真结果

4 参考文献

[1]任浩然. 基于自适应遗传算法优化的BP神经网络股价预测模型[D]. 延安大学.

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