pre_lya_change.m

本文是通过文中方法来求最小嵌入维程序.子程序,相重构程序.

 function pre_lya_change(data,tau,m,P,lmd,MaxStep)
%单步预测
% clc

% tau=1;
% m=35;
% P=2;
% lmd=.016;

% disp('--------最大预测步数---------')
%  MaxStep=round(1/lmd)
 
% MaxStep=5;%避免预测太长控制在10步
% lmd=lmd1;
%  MaxStep=PreStep;
prestep=MaxStep;

%-----------最近距离不考虑相角--------------
for step=1:prestep
    step
    newdata1=data(1:length(data)-prestep+step-1);
    [idx,min_d,idx1,min_d1]=nearest_point(tau,m,newdata1,length(newdata1),P);
    %[x_1,x_2]=prebylya_new(newdata1,m,tau,lmd,P,idx);
   % [x_1,x_2]=pre_by_lya_new(m,lmd,newdata1,length(newdata1),P,idx1);
    [x_1,x_2]=pre_by_lya1(tau,m,lmd,newdata1,length(newdata1),idx1,min_d1,1);
    pre_value1(step)= x_1;
end
%-----------最近距离考虑相角--------------
% for step=1:prestep
%     step
%     newdata2=data(1:length(data)-prestep+step-1);
%     [idx,min_d,idx1,min_d1]=nearest_point(tau,m,newdata2,length(newdata2),P);
%     [x_1,x_2]=pre_by_lya1(tau,m,lmd,newdata2,length(newdata2),idx,min_d,1);
%     pre_value2(step) = x_1;
% end
figure
plot(length(data)-prestep+1:length(data),data(length(data)-prestep+1:length(data)),'b-',length(data)-prestep+1:length(data),pre_value1,'r*-')
% hold on
%plot(length(data)-prestep+1:length(data),pre_value2,'m--');
 legend('实测值','lyap法预测值');%,'Predict(考虑相角)');
% disp('-----------求标准差-----------------')
% delt1=std(data(length(newdata)+1:length(data))-newdata1(length(newdata)+1:length(data)))
% delt2=std(data(length(newdata)+1:length(data))-newdata2(length(newdata)+1:length(data)))

disp('-----------求RMSE----------------------')
old=data(length(data)-prestep+1:length(data))';
q=mean(old);
RMSE1=norm(pre_value1-old,2)/norm(old-q,2)
%RMSE2=norm(pre_value2-old,2)/norm(old-q,2)

disp('-------------中误差-----------------')
v=abs(pre_value1-old);
MSE=sqrt(sum(v.^2)/length(old))


disp('----------MAPE平均绝对百分比误差---------------')
for i=1:length(old)
    a2(i)=pre_value1(i)-old(i);
    a1(i)=abs((old(i)-pre_value1(i))/old(i));
%    a2(i)=abs((old(i)-pre_value2(i))/old(i));

end
MAPE1=sum(a1)/length(old)*100
%MAPE2=sum(a2)/length(old)*100
figure
plot(a2)
b=max(a2)-min(a2);
axis([1 length(a2) min(a2)-b max(a2)+b])
grid;xlabel('预测步数');ylabel('误差')

xuhao=(length(data)-prestep+1:length(data));
jieguo=[xuhao;pre_value1;old;a2;a1]';