Path: utzoo!attcan!uunet!know!zaphod.mps.ohio-state.edu!wuarchive!udel!rochester!bbn.com!archive.bbn.com!aboulang From: aboulang@bbn.com (Albert Boulanger) Newsgroups: comp.ai.neural-nets Subject: Re: Weather forecasting References Message-ID: Date: 13 Oct 90 20:01:29 GMT References: <1990Oct13.042132.27956@ecn.purdue.edu> Sender: news@bbn.com Reply-To: aboulanger@bbn.com Organization: BBN, Cambridge MA Lines: 27 In-reply-to: muttiah@stable.ecn.purdue.edu's message of 13 Oct 90 04:21:32 GMT In article <1990Oct13.042132.27956@ecn.purdue.edu> muttiah@stable.ecn.purdue.edu (Ranjan S Muttiah) writes: -- One paper I have found particularly interesting is Predicting the future: A Connectionist Approach A.S Weigend, B.A Huberman and D.E Rumelhart, 1990 Stanford Universtity technical report Standford-PDP-90-01 Submitted to the International Journal of Neural Systems. One other excellent paper that represents this approach (chaotic time series prediction) is: "Nonlinear Forcasting as a Way of Distinguishing Chaos from Measurement Error in Time Series" George Sugihara & Robert M. May Nature, Vol344, 19 April 1990, 734-741 They give examples where the nonlinear perdiction technique works AND (contrary to what one sees in the AI world) does not work. Regards, Albert Boulanger aboulanger@bbn.com