From: Greg Heath <>
Newsgroups: comp.soft-sys.matlab
Subject: Re: neural network: out of memory error
Date: Mon, 3 Jan 2011 08:22:44 -0800 (PST)
Lines: 91
Message-ID: <>
References: <ifqc0d$t3o$> <>
Mime-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
X-Trace: 1294071765 9121 (3 Jan 2011 16:22:45 GMT)
NNTP-Posting-Date: Mon, 3 Jan 2011 16:22:45 +0000 (UTC)
Injection-Info:; posting-host=; posting-account=mUealwkAAACvQrLWvunjg50tRAnsNtJR
User-Agent: G2/1.0
X-HTTP-UserAgent: Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; .NET CLR
 2.0.50727; .NET CLR 3.0.4506.2152; .NET CLR 3.5.30729),gzip(gfe)
Xref: comp.soft-sys.matlab:699884

On Jan 3, 9:33 am, "pietro " <> wrote:
> Greg Heath <> wrote in message <>...
> > On Jan 2, 12:18 pm, "pietro " <> wrote:
> > > Hi,
> > > I'm using neural network toolbox 7, in particular the tool for predicting time series (ntstool). I have a neural network with 20 neurons, 25 delay, but I get out of memory error. In this link
> > > I think the network dimensions are too high for my pc.
> > In fact, both values may be unnecessarily high for a good
> > solution to the problem.
> > See my recent 4 post thread for determining both d (the
> > number of delays) from the autocorrelation function and
> > the minimum reasonable value for H (the number of hidden
> > nodes).
> >
> > Hope this helps.
> > Greg
> Hi Greg,
> it is very interesting your post thread, but I have found some difficulties to apply it in my case. I need a neural network for predicting the response of mechanical system, so I have measured the input signal and the output signal and from this I want to estimate the system transfer function.

Please implement carriage returns so that your paragraphs
are not single lines when cut and pasted into Windows Notepad.

Tranfer functions are only defined for linear systems.
If you have a linear system, you don't need a neural network

So,... I am guessing that you have a nonlinear system and
want to model the I/O characteristics with a NN.

>The signal sampling rate is 500Hz.

Insufficient information. How long is your signal?

>I have triend to use the xcorr like you have mentioned in your example, in this way:
> acf = xcorr(input,output);
> acfss = acf(N:end)/max(acf);
> [maxacf1 d] = max(acfss(2:end))
> but I get
> maxacf1 =
>      1
> d =
>         1816
> What does it mean this delay? I need a neural network with 1816 number of delays?

Insuffient information.

It could indicate periodicity.
What is N?
What does your plot of acfss look like?
Can you post it on a website so I can see it?
Can you send me input and output in a text file?

> Moreover I haven't understood the principle ideas for selecting the hidden neurons number. May you give me further explanations?
> I hope to have well explained my doubts, if not, don't hesitate to ask me.

Reference your questions about choosing H
w.r.t. my code statements.

Also search in the CSSM archives using

greg heath Neq Nw

Hope this helps.