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From: mehra@aquinas.csl.uiuc.edu (Pankaj Mehra)
Newsgroups: comp.ai.neural-nets
Subject: Re: Prediction
Keywords: bp, kohonen, system, outage, TD(l), Sutton
Message-ID: <1990Sep21.063517.6498@ux1.cso.uiuc.edu>
Date: 21 Sep 90 06:35:17 GMT
References: <6610@aplcen.apl.jhu.edu>
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Reply-To: mehra%aquinas@uxc.cso.uiuc.edu (Pankaj Mehra)
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In article <6610@aplcen.apl.jhu.edu> simonof@aplcen.apl.jhu.edu (Simonoff Robert  301 540 1864) writes:
>    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.

This problem should be solvable using the family of detection algorithms
described by Sutton ("Learning to Predict ..", Machine Learning, v. 3, 1988).

Also, I am surprised that you are ignoring the simplest and most well-studied
techniques of linear prediction (FIR and IIR filters). The following
references should help you get started:

Hamming, "Digital Filters", 3 ed., 1989, Prentice-Hall
	if you know your undergrad. math, you can read this book in two days
Widrow and Stearns, "Adaptive Signal Processing", 1985, Prentice-Hall
	examples of adaptive recursive filter design
Goodwin and Sin, "Adaptive Filtering, Prediction, and Control",??,Prentice-Hall
	more advanced text describes stochastic predicition, Kalman filters

-Pankaj {Mehra@cs.uiuc.edu}
-- 
Pankaj Mehra
e-mail: {mehra@cs., mehra@aquinas.csl., p-mehra@}uiuc.edu
phone: (217)244-7176