HI, please help me to forecast feature groundwater level using feed forward neural network with LM algorithm, i developed the feed forward neural network with LM algorithm with ANN architecture 3-5-1, my model is as follows, but i want to use this model for forecasting at least 3 to 6 point ahead, can any one please help me,
clear all;
clc;
load dannlm.txt;
t=dannlm(:,1);
rain=dannlm(:,2);
et=dannlm(:,3);
gwl=dannlm(:,4);
%[ACF, Lags, Bounds] = autocorr(gwl, [], 2);
%[PACF, Lags, Bounds] = parcorr(gwl, [], 2);
%[CCF, Lags, Bounds] = crosscorr(gwl,rain);
gwlmin=min(gwl);
gwlmax=max(gwl);
rain=(rain-min(rain))/(max(rain)-min(rain));
et=(et-min(et))/(max(et)-min(et));
gwl=(gwl-min(gwl))/(max(gwl)-min(gwl));
%*****Data Preparation****
for t=5:195
Data(t-4,:)=[rain(t-4) et(t-1) gwl(t-1) gwl(t)];
end
%Define Input patter for Training and Validation
INPTR=Data(1:150, 1:3);
TARTR=Data(1:150,4);
net=newff([0 1; 0 1; 0 1], [5 1], {'logsig', 'purelin'}, 'trainlm');
net.trainParam.epochs=500;
net.trainParam.goal=0.0001;
net.performFcn='mse';
net=init(net);
net=train(net, INPTR', TARTR');
a=sim(net, INPTR');
z=[a' TARTR];
INPVAL=Data(151:191, 1:3);
TARVAL=Data(151:191,4);
y=sim(net, INPVAL');
zv=[y' TARVAL];
%Converting back to Original Flow of gwl Validation
zv=zv*(gwlmax-gwlmin)+gwlmin;
save val1.txt zv -ascii;
z=z*(gwlmax-gwlmin)+gwlmin;
save cal1.txt z -ascii;
CORR_CAL=corrcoef(z)
CORR_VAL=corrcoef(zv)
COM_CAL=z(:,1);
OBS_CAL=z(:,2);
COM_VAL=zv(:,1);
OBS_VAL=zv(:,2);
Eff_ANN_CAL=1-(sumsqr(OBS_CAL-COM_CAL)/sumsqr(OBS_CAL-mean(OBS_CAL)))
Eff_ANN_VAL=1-(sumsqr(OBS_VAL-COM_VAL)/sumsqr(OBS_VAL-mean(OBS_VAL)))
RMSE_ANN_CAL=sqrt(sumsqr(OBS_CAL-COM_CAL)/length(OBS_CAL))
RMSE_ANN_VAL=sqrt(sumsqr(OBS_VAL-COM_VAL)/length(OBS_VAL))
Ex_Var_ANN_CAL=sqrt(sumsqr(COM_CAL-mean(OBS_CAL))/sumsqr(OBS_CAL-mean(OBS_CAL)))
%ERROR_CAL=100*(OBS_CAL-COM_CAL)/OBS_CAL;
%ERROR_VAL=100*(OBS_VAL-COM_VAL)/OBS_VAL;
ANN_PEAK_CAL=(1-max(COM_CAL)/max(OBS_CAL))*100
ANN_PEAK_VAL=(1-max(COM_VAL)/max(OBS_VAL))*100

댓글 수: 1

Anand Kumar
Anand Kumar 2015년 5월 8일
Please convert above attachment to forecast at least 3 to 6 point ahead

댓글을 달려면 로그인하십시오.

 채택된 답변

Greg Heath
Greg Heath 2015년 5월 11일

0 개 추천

help narxnet
doc narxnet
net = narxnet(3:6,3:6,5);
===================================================================
% ===>GEH1: WHAT DO THE NAMES DANNLM AND ET STAND FOR?
% ===> GEH2: AUTOCORR(gwl,...), CROSSCORR(GWL,RAIN,...) AND % CROSSCORR(GWL,ET,...) CAN BE USED TO FIND THE SIGNIFICANT DELAYS.
for t=5:195
Data(t-4,:)=[rain(t-4) et(t-1) gwl(t-1) gwl(t)];
end
% ===>GEH3: What is the rationale for this combination?
% ===> GEH4: NEWFF has been obsolete for at least 5 years. Regardless, this is a time series problem which is more easily solved using NARXNET or it's obsolete predecessor.
z=[a' TARTR];
% ===>GEH5: Why not compute the error mse(a'-TARTR) ???
INPVAL=Data(151:191, 1:3);
TARVAL=Data(151:191,4);
% ===> GEH6: This is TEST data, NOT VALIDATION DATA! VALIDATION DATA IS USED TO TUNE PARAMETERS. TEST DATA IS USED TO OBTAIN UNBIASED ESTIMATES OF PERFORMANCE
% THAT IS AS FAR AS I WENT. AFTER CALCULATING
NMSE = mse(error)/mean(var(target',1) and/or
R2 = 1-NMSE
%SUCCESS OR FAILURE IS DETERMINED
Hope this helps.
Thank you for formally accepting my answer
Greg

추가 답변 (0개)

카테고리

도움말 센터File Exchange에서 Get Started with MATLAB에 대해 자세히 알아보기

태그

질문:

2015년 5월 8일

편집:

2015년 5월 11일

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by