1.完整项目描述和程序获取
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2.部分仿真图预览
3.算法概述
4.部分源码
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%仿真时间大于30分钟，请耐心等待
MAXGEN = 50;
NIND = 20;
Nums = 1;
Chrom = crtbp(NIND,Nums*10);
%sh
Areas = [];
for i = 1:1
Areas = [Areas,[1;100]];% 目标范围0到4，如果是0，则表明该位置不安装充电桩
end
FieldD = [rep([10],[1,Nums]);Areas;rep([0;0;0;0],[1,Nums])];
gen = 0;
Js = 0.5*rand(NIND,1);
Objv = (Js+eps);
gen = 0;
while gen < MAXGEN
gen
Pe0 = 0.999;
pe1 = 0.001;
FitnV=ranking(Objv);
Selch=select('sus',Chrom,FitnV);
Selch=recombin('xovsp', Selch,Pe0);
Selch=mut( Selch,pe1);
phen1=bs2rv(Selch,FieldD);
for a=1:1:NIND
X = phen1(a);
%计算对应的目标值
[epls] = func_obj(X);
E = epls;
JJ(a,1) = E;
end
Objvsel=(JJ);
[Chrom,Objv]=reins(Chrom,Selch,1,1,Objv,Objvsel);
gen=gen+1;
Error2(gen) = mean(JJ);
end
figure
plot(smooth(Error2,MAXGEN),'linewidth',2);
grid on
xlabel('迭代次数');
ylabel('遗传算法优化过程');
legend('Average fitness');
[V,I] = min(JJ);
X = phen1(I);
numFeatures = 2;
numResponses = 1;
numHiddenUnits = round(X);% 定义隐藏层中LSTM单元的数量
layers = [ ...% 定义网络层结构
sequenceInputLayer(numFeatures)
gruLayer(numHiddenUnits)
dropoutLayer(0.1)
gruLayer(2*numHiddenUnits)
dropoutLayer(0.1)
fullyConnectedLayer(numResponses)
regressionLayer
];
ypred = predict(net,[P],'MiniBatchSize',1);
figure;
subplot(211);
plot(T)
hold on
plot(ypred)
xlabel('days');
ylabel('负荷');
legend('实际负荷','GRU预测负荷');
subplot(212);
plot(T-ypred)
xlabel('days');
ylabel('GRU误差');
save R2.mat T ypred
0X_056m
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