Matlab Elman预测之神经网络的电力负荷预测模型【含Matlab源码 279期】.zip

%% 基于模拟退火改进狮群算法elmk预测 clc; clear all close all nntwarn off; %% 数据载入 load data; a=data; %% 选取训练数据和测试数据 N=size(a,1);%样本数量 M=size(a,2);%数据维度 rate=0.6;%采样率 % 训练数据输入 for i=1:N-3 p(i,:)=[a(i,:),a(i+1,:),a(i+2,:)]; end p_train=a(1:round(N*rate),1:M-1); % 训练数据输出 t_train=a(1:round(N*rate),M); % 测试数据输入 p_test=a((round(N*rate)+1):N,1:M-1); % 测试数据输出 t_test=a((round(N*rate)+1):N,M); % 为适应网络结构 做转置 %% 数据归一化 p_train=p_train';%n*m数据，n是输入特征数量，m是样本数量 t_train=t_train';%n*m数据，n是输入特征数量，m是样本数量 [p_train, ps_input] = mapminmax(p_train,0,1); [t_train, ps_output] = mapminmax(t_train,0,1); threshold=[0 2;0 2];%几个n几个[0,1] shuchu=1;%输出个数 % [p_test ] = mapminmax(p_test' ,0,1); p_test = mapminmax('apply',p_test',ps_input); %统计结果 pop=30; % 种群数量 Max_iter=50; % 设定最大迭代次数 dim=1;%隐藏层 beta = 0.5;%成年狮所占比列 Nc = round(pop*beta);%成年狮数量 Np = pop-Nc;%幼师数量 lb=[2]; ub=[15]; if(max(size(ub)) == 1) ub = ub.*ones(1,dim); lb = lb.*ones(1,dim); end %种群初始化 X0=initialization(pop,dim,ub,lb); X = X0; %计算初始适应度值 fitness = zeros(1,pop); for i = 1:pop [rmse,y] = elm_fun(p_test,t_test,p_train,t_train,threshold,shuchu,X(i,:), ps_output); fitness(i) = rmse; end T=max(fitness)-min(fitness);%初始温度 [value, index]= min(fitness);%找最小值 GBestF = value;%全局最优适应度值 GBestX = X(index,:);%全局最优位置 GBestY=y;%预测值 curve=zeros(1,Max_iter); XhisBest = X; fithisBest = fitness; indexBest = index; gbest = GBestX; for t = 1: Max_iter t %母狮移动范围扰动因子计算 stepf = 0.1*(mean(ub) - mean(lb)); alphaf = stepf*exp(-30*t/Max_iter)^10; %幼狮移动范围扰动因子计算 alpha = (Max_iter - t)/Max_iter; %母狮位置更新 for i = 1:Nc index = i; while(index == i) index = randi(Nc);%随机挑选一只母狮 end X(i,:) = (X(i,:) + X(index,:)).*(1 + alphaf.*randn())./2; end %幼师位置更新 for i = Nc+1:pop q=rand; if q<=1/3 X(i,:) = (gbest + XhisBest(i,:)).*( 1 + alpha.*randn())/2; elseif q>1/3&&q<2/3 indexT = i; while indexT == i indexT = randi(Nc) + pop - Nc;%随机位置 end X(i,:) = (X(indexT,:) + XhisBest(i,:)).*( 1 + alpha.*randn())/2; else gbestT = ub + lb - gbest; X(i,:) = (gbestT + XhisBest(i,:)).*( 1 + alpha.*randn())/2; end end %边界控制 for j = 1:pop for a = 1: dim if(X(j,a)>ub) X(j,a) =ub(a); end if(X(j,a)<lb) X(j,a) =lb(a); end end end %计算适应度值 for j=1:pop [rmse,y] = elm_fun(p_test,t_test,p_train,t_train,threshold,shuchu,X(j,:), ps_output); fitness(j) = rmse; end for j = 1:pop if(fitness(j)>fithisBest(j)) XhisBest(j,:) = X(j,:); fithisBest(j) = fitness(j); end if(fitness(j) >GBestF) GBestF = fitness(j); GBestX = X(j,:); indexBest = j; end end C=max(fitness)-min(fitness); S=[1,exp(-C/T)]; if rand<min(S) %% 狮王更新 Temp = gbest.*(1 + randn().*abs(XhisBest(indexBest,:) - gbest)); Temp(Temp>ub)=ub(Temp>ub); Temp(Temp<lb) = lb(Temp<lb); [rmse,y] = elm_fun(p_test,t_test,p_train,t_train,threshold,shuchu,Temp, ps_output); else Temp = gbest; [rmse,y] = elm_fun(p_test,t_test,p_train,t_train,threshold,shuchu,Temp, ps_output); end fitTemp = rmse; if(fitTemp<GBestF) GBestF =fitTemp; GBestX = Temp; GBestY= y; X(indexBest,:)=Temp; fitness(indexBest) = fitTemp; curve(t) = GBestF; else curve(t) = GBestF; GBestY=GBestY; GBestX = GBestX; end % [value, index]= min(fitness);%找最小值 % gbest = X(index,:);%当前代，种群最优值 T=0.9*T;%更新温度 end Best_pos = GBestX; Best_score = curve(end); figure plot(curve,'Color','r','linewidth',1.5) title(['模拟退火改进狮群优化elmk预测',num2str(Best_score)]) xlabel('Iteration'); ylabel('Best score obtained so far');