NARX network cannot learn identity relation

2015 1 3
1 个回答
回答已采纳
更新时间：2015 1 11
1 次查看（30 天）

0 个投票

Consider the simple network below, where targets are simply the inputs. I was expecting the network learn this relation perfectly but unfortunately this is not the case.
Can someone please clarify why this doesnt work?
X = rand(1,1000);
T = X;
net = narxnet(1:2,1:2,10);
[x,xi,ai,t] = preparets(net,con2seq(X),{},con2seq(T));
[net,tr] = train(net,x,t,xi,ai);

请先登录，再回答此问题。

 采纳的回答

Greg Heath
Greg Heath 2015-1-5
编辑：Greg Heath 2015-1-5

1 个投票

For meaningful examples, always use the MATLAB EXAMPLE DATA obtained from
help nndatasets
doc nndatasets
Your example is a case of a single series target without an exogeneous external input. Therefore, the appropriate time-series function would be NARNET.
For meaningful examples, always use data with a deterministic I/O relationship. A random time series is not correlated to anything but a zero delay copy of itself. That would mean a narnet with a zero feedback delay. However, for obvious reasons, zero feedback delays are not allowed in time series.
Hope this helps
Thank you for formally accepting my answer
Greg

4 个评论
显示 2更早的评论 隐藏 2更早的评论

Hakan
Hakan 2015-1-5
Thanks for the answer but I cant get why the NAR or NARX network cannot learn this relation. A feedforwardnet perfectly learns it. I was expecting the network will simply learn to ignore past values (non zero delays).
Consider another example, where X (exogeneous input) is the random force applied to a unit mass and T is the velocity of the mass (code is below). This is a deterministic relationship with no noise, right? NARX also cannot learn this very well. I then changed the sample to identity relation to understand what is going on.
In this physics sample, T is not a function of (T,X) but the change of T (derivative of T) is a function of (T,X). Does this effect learning, if so how to model it properly?
The code of this simple physics network. 1000 samples x 100 timeteps
num_series = 1000;
x_parts = cell(1, num_series);
t_parts = cell(1, num_series);
for i=1:num_series
x_ = randn(1,100); % force applied to unit mass
t_ = cumsum(x_); % velocity of the mass. simply the cumulated sum of forces
t_parts{i} = con2seq(t_);
x_parts{i} = con2seq(x_);
end;
T = catsamples(t_parts{:}, 'pad');
X = catsamples(x_parts{:}, 'pad');
num_delays = 2;
net = narxnet(1:num_delays,1:num_delays,10);
net.divideFcn = 'divideind';
net.divideMode = 'sample';
[net.divideParam.trainInd, net.divideParam.valInd, net.divideParam.testInd] = divideblock(num_series, 0.6, 0.2, 0.2);
[x,xi,ai,t] = preparets(net,X,{},T);
[net,tr] = train(net,x,t,xi,ai);
Greg Heath
Greg Heath 2015-1-6
Your net cannot learn
y(t) = x(t);
because you have imposed ID=1:2, FD=1:2 which constrains it to functions of the form
y(t) = f( x(t-1), x(t-2), y(t-1), y(t-2) ).
Try again using
ID = 0:2, FD= 1:2.
However, the result will not be an identity operator for any other function besides this particular x(t);
Hope this helps.
Thank you for formally accepting my answer
Greg
Hakan
Hakan 2015-1-6
ah, of course, thx:)
a final question, now the network perfectly learns the simple physics system, error is about e-11. but the error autocorrelation does not fall into confidence interval as can be seen in the attached image. indeed as the error decreases error autocorrelation spread out of confidence interval, how should I interpret this?
Greg Heath
Greg Heath 2015-1-11
编辑：Greg Heath 2015-1-11
Great question!!!
Except for the fact that the value at zero lag, 11e-12, is the mean square error, I'm not sure how helpful this is.

请先登录，再进行评论。

更多回答（0 个）

类别

在 帮助中心 和 File Exchange 中查找有关 Deep Learning Toolbox 的更多信息

产品

标签

Community Treasure Hunt

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

Start Hunting!

Translated by