Why the results of my Elman network are different every time?

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I created a elman network. But the results every time I run the code were different.I got different "errors","regression" and "avg_error" Could anyone tell me why? Appreciate SO MUCH!
Here is the code.
clear all
load('input4_train.mat');
load('output4_train.mat');
load('input4_test.mat');
load('output4_test.mat');
inputSeries = tonndata(input4_train,false,false);
targetSeries = tonndata(output4_train,false,false);
inputTest = tonndata(input4_test,false,false);
outputTest = tonndata(output4_test,false,false);
% Create a Network
hiddenLayerSize = 5;
net=newelm(inputSeries,targetSeries,[10,3,1], {'tansig','logsig','purelin'});
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
net.trainParam.epochs = 2000;
% Initial net
net = init(net);
% Train the Network
net = adapt(net,inputSeries,targetSeries);
% Test the Network
outputs = sim(net,inputTest);
errors = gsubtract(outputTest,outputs);
error = cell2mat(errors);
for i = 1:10
error(i)=abs(error(i));
end
avg_error = sum(error)/10;
performance = perform(net,outputTest,outputs)
% View the Network
view(net)
% Plots
figure, plotregression(outputTest,outputs)
figure, plotresponse(outputTest,outputs)
figure, ploterrcorr(errors)
  3 Comments
Greg Heath
Greg Heath on 1 Aug 2015
Edited: Walter Roberson on 2 Aug 2015
% load('input4_train.mat');
% load('output4_train.mat');
% load('input4_test.mat');
% load('output4_test.mat');
%
% inputSeries = tonndata(input4_train,false,false);
% targetSeries = tonndata(output4_train,false,false);
% inputTest = tonndata(input4_test,false,false);
% outputTest = tonndata(output4_test,false,false);
whos
% % Create a Network
% hiddenLayerSize = 5;
Value never used
% net=newelm(inputSeries,targetSeries,[10,3,1], {'tansig','logsig','purelin'});
No justification for 3 hidden layers. One is sufficient.
% % Setup Division of Data for Training, Validation, Testing
% net.divideParam.trainRatio = 70/100;
% net.divideParam.valRatio = 15/100;
% net.divideParam.testRatio = 15/100;
Above 3 commands unnecessary for default values.
% net.trainParam.epochs = 2000;
%
% % Initial net
% net = init(net);
%
% % Train the Network
% net = adapt(net,inputSeries,targetSeries);
%
% % Test the Network
% outputs = sim(net,inputTest);
%
% errors = gsubtract(outputTest,outputs);
% error = cell2mat(errors);
% for i = 1:10
% error(i)=abs(error(i));
% end
% avg_error = sum(error)/10;
Above 5 commands unnecessary.
help mae
doc mae
% performance = perform(net,outputTest,outputs)
% % View the Network
% view(net)
%
% % Plots
% figure, plotregression(outputTest,outputs)
% figure, plotresponse(outputTest,outputs)
% figure, ploterrcorr(errors)
Defaults: Above 3 commands unnecessary;
Heather Zhang
Heather Zhang on 2 Aug 2015
Thank you so much for your detailed,Greg! I need your help on my code. I will answer your questions.
Q2. What scenario did you use it for? Why was it used instead of timedelaynet or narxnet? I use it for dealing with a time series prediction issue.
Q3. Where did you obtain this code? Many of the options chosen make little sense. I coded it according to BP network code. I knew "newelm" function is for build an Elman network and "sim" is for testing a network. So I coded it by myself. I can tell there should be something wrong in it, but I don't know where the mistakes are.
So pleas help me on this code, and that would be appreciated very much!

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Accepted Answer

Walter Roberson
Walter Roberson on 31 Jul 2015
Neural Networks initialize their weights randomly usually. If you want repeatability you can initialize the weights yourself or you can set the random number generator seed.
  2 Comments
Heather Zhang
Heather Zhang on 2 Aug 2015
Thank you so much,Walter! I use 'init' function to initialize the weights. Am I right? Looking forward to your answer.

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