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From: simonof@aplcen.apl.jhu.edu (Simonoff Robert  301 540 1864)
Newsgroups: comp.ai.neural-nets
Subject: Prediction
Summary: prediction of system outages using bp or Kohonen
Keywords: bp, kohonen, system, outage
Message-ID: <6610@aplcen.apl.jhu.edu>
Date: 21 Sep 90 04:30:40 GMT
Distribution: comp
Organization: Johns Hopkins University, Laurel, MD
Lines: 40


 
Hi,
 
I am working on a problem where I must be able to predict
the outage of a system early enough so that humans can
interveve and prevent the outage.  Earlier I asked about
using Kohonen networks as novelty detectors (as per
Kohonen's book), and was not able to get any answers on how
self organizing feature maps can be used as novelty
detectors - this was my first approach to solving the outage
prediction problem.
 
I would like to ask the question in reference to back
propagation - how would one go about solving the following
type of problem using bp:
 
    You have data representing the system state.  There is a
    lot of variables that can be fed into the network, but
    it is unknown exactly at what point the data represents
    a state which is stable and at what point the data
    represents a state which will lead to a system outage.
    My first thought was to consider the last recorded
    system state as an unstable state and train the network
    to descren this is outage eminant.  But what of the
    state data 2 seconds earlier, is that bad or not?? (no
    one knows for sure).
 
    Is the only answer to assume the last recorded system
    state is outage predicting (say output of 0.0), use
    analog outputs and if the network output < 0.3 or 0.4,
    consider the system in trouble??
 
    The question is how might one go about this using a
    supervised network?
 
Thanks,
Bob Simonoff
simonof@aplcen.apl.jhu.edu 
(301) 240-3168