【预测模型】基于差分进化算法优化BP神经网络实现数据预测matlab源码

1 算法介绍

模型介绍见这里。

2 部分代码

%% 差分进化算法应用于优化BP神经网络的初始权值和阈值

%% 清空环境变量
clear all;
clc;
warning off
load v357;
load y357;
Pn_train=v;
Tn_train=y;
Pn_test=v;
Tn_test=y;
P_train=v;
 T_train=y;
% P_train=[0 25.27 44 62.72 81.4 100.2;
%     290.5 268.8 247.2 224.5 206 184.4;
%     0 16.12 33.25 50.42 67.62 84.73;
%     542.5 517.8 493 465.3 435.6 410.8;
%     0 11.1 28.1 44.93 61.38 78.57;
%     826.1 800.2 769.1 740.0 706.2 669.3];
% T_train=[0 1 2 3 4 5];%以上是未处理的数据
%  P_test=[0 25.25 43 62.75 81.6 100.7;
%     290.3 268.4 247.5 224.6 206 184.2;
%     0 16.14 33.26 50.47 67.68 84.79;
%     542.7 517.9 495 465.8 435.6 410.9;
%     0 11.4 28.6 44.94 61.36 78.59;
%     826.3 800.7 769.8 740.5 706.7 669.3];
% T_test=[0 1 2 3 4 5];

% Pn_train=[0 0.252 0.439 0.626 0.813 1 0 0.19 0.392 0.595 0.798 1 0 0.141 0.358 0.572 0.781 1;
%           1 0.795 0.592 0.378 0.204 0 1 0.815 0.626 0.415 0.189 0 1 0.835 0.637 0.451 0.235 0];
% %T 为目标矢量 ，归一化后的数据
% Tn_train=[0.05,0.23,0.41,0.59,0.77,0.95,0.05,0.23,0.41,0.59,0.77,0.95,0.05,0.23,0.41,0.59,0.77,0.95]; 
% Pn_test=[ 0 0.17 0.39 0.595 0.798 1 0 0.141 0.358 0.572 0.781 1 0 0.258 0.439 0.626 0.813 1;
%          1 0.815 0.625 0.415 0.189 0 1 0.835 0.635 0.451 0.235 0 1 0.795 0.599 0.378 0.204 0 ];
% Tn_test=[0.05,0.23,0.41,0.59,0.77,0.95,0.05,0.23,0.41,0.59,0.77,0.95,0.05,0.23,0.41,0.59,0.77,0.95];

%% 参数设置
S1 = size(Pn_train,1);              % 输入层神经元个数
S2 = 6;                            % 隐含层神经元个数
S3 = size(Tn_train,1);              % 输出层神经元个数
Gm=10;    %最大迭代次数
F0=0.5;      %F为缩放因子
Np=5; %种群规模
CR=0.5;  %杂交参数
G=1;%初始化代数
N=S1*S2 + S2*S3 + S2 + S3;%所求问题的维数


% 设置网络初始权值和阈值
net_optimized.IW{1,1} = W1;
net_optimized.LW{2,1} = W2;
net_optimized.b{1} = B1;
net_optimized.b{2} = B2;
% 设置训练参数
net_optimized.trainParam.epochs = 3000;
net_optimized.trainParam.show = 100;
net_optimized.trainParam.goal = 0.001;
net_optimized.trainParam.lr = 0.1;

% 利用新的权值和阈值进行训练
net_optimized = train(net_optimized,Pn_train,Tn_train);

%% 仿真测试
Tn_sim_optimized = sim(net_optimized,Pn_test);     


% 结果对比
result_optimized = [Tn_test' Tn_sim_optimized'];

%均方误差
E_optimized = mse(Tn_sim_optimized - Tn_test)
MAPE_optimized = mean(abs(Tn_sim_optimized-Tn_test)./Tn_sim_optimized)*100

% figure(1)
% 
% plot(T_train,P_train(1,:),'r')
% hold on 
% plot(T_train,P_train(3,:),'y')
% hold on
% plot(T_train,P_train(5,:),'b')
% hold on
% grid on
% xlabel('标准设备的约定真值（10KP)');
%  ylabel('压力传感器的输出（mv）');
%  title('压力传感器的工作曲线');
%  legend('t=22','t=44','t=70');

 figure(2)
plot(Tn_train(1:6),Pn_train(1,1:6),'r')
hold on 
plot(Tn_train(7:12),Pn_train(1,7:12),'y')
hold on
plot(Tn_train(13:18),Pn_train(1,13:18),'b')
hold on
grid on
xlabel('设备约定真值（10KP)');
 ylabel('压力传感器的输出（mv）');
 title('归一化后的训练样本压力传感器的工作曲线');
 legend('t=22','t=44','t=70');
 
 figure(3)
plot(Tn_test(1:6),Pn_test(1,1:6),'r')
hold on 
plot(Tn_test(7:12),Pn_test(1,7:12),'y')
hold on
plot(Tn_test(13:18),Pn_test(1,13:18),'b')
hold on
grid on
xlabel('设备约定真值（10KP)');
 ylabel('压力传感器的输出（mv）');
 title('归一化后的测试样本压力传感器的工作曲线');
 legend('t=22','t=44','t=70');
 
 
 figure(4)
plot(Tn_test(1:6),Tn_sim_optimized(1:6),'r')%输出DE-BP仿真结果的曲线
hold on
 plot(Tn_test(7:12),Tn_sim_optimized(7:12),'y')
 hold on
 plot(Tn_test(13:18),Tn_sim_optimized(13:18),'b')
 hold on
 xlabel('约定真值（10KP)');
 ylabel('压力传感器的输出（mv）');
 title('DE-BP的压力传感器的工作曲线');
 legend('t=22','t=44','t=70');
grid on








%% 未优化的BP神经网络
%net = newff(Pn_train,Tn_train,S2);
net=newff(minmax(Pn_train),[6,1],{'logsig','purelin'},'traingdm');%隐含层神经元S型正切,输出层S型对数，动量梯度下降法训练BP网络,
% 设置训练参数
net.trainParam.epochs = 3000;
net.trainParam.show = 100;
net.trainParam.goal = 0.001;
net.trainParam.lr = 0.1;

net=init(net);
 
inputWeights=net.IW{1,1};% 当前输入层权值和阈值
inputbias=net.b{1};
layerWeights=net.LW{2,1};% 当前网络层权值和阈值 
layerbias=net.b{2}
% 利用新的权值和阈值进行训练
net = train(net,Pn_train,Tn_train);

%% 仿真测试
Tn_sim = sim(net,Pn_test);    


%% 结果对比
result = [Tn_test' Tn_sim'];
% 均方误差
E1 = mse(Tn_sim - Tn_test)
MAPE1= mean(abs(Tn_sim-Tn_test)./Tn_sim)*100
% end
% figure(4)
% plot(T_train,P_train(1,:),'r')
% hold on 
% plot(T_train,P_train(3,:),'y')
% hold on
% plot(T_train,P_train(5,:),'b')
% hold on
% grid on
% xlabel('标准设备的约定真值（10KP)');
%  ylabel('压力传感器的输出（mv）');
%  title('压力传感器的工作曲线');
%  legend('t=22','t=44','t=70');

 figure(5)
plot(Tn_test(1:6),Tn_sim(1:6),'r')%输出BP仿真结果的曲线
hold on
 plot(Tn_test(7:12),Tn_sim(7:12),'y')
 hold on
 plot(Tn_test(13:18),Tn_sim(13:18),'b')
 hold on
 xlabel('约定真值（10KP)');
 ylabel('压力传感器的输出（mv）');
 title('BP的压力传感器的工作曲线');
 legend('t=22','t=44','t=70');
grid on

3 仿真结果

4 参考文献

[1]牛庆,曹爱民,陈潇一,周冬.基于花朵授粉算法和BP神经网络的短期负荷预测[J].电网与清洁能源,2020,36(10):28-32.