Path: utzoo!utgpu!news-server.csri.toronto.edu!bonnie.concordia.ca!uunet!mcsun!ukc!cam-eng!ajr From: ajr@eng.cam.ac.uk (Tony Robinson) Newsgroups: comp.ai.neural-nets Subject: Re: optimization using neural networks Message-ID: <22568@rasp.eng.cam.ac.uk> Date: 11 Feb 91 13:24:34 GMT References: Sender: ajr@eng.cam.ac.uk Distribution: comp.ai.neural-nets Organization: Cambridge University Engineering Department, UK Lines: 45 Newsgroups: comp.ai.neural-nets Subject: Re: optimization using neural networks Summary: Expires: References: Sender: Followup-To: Distribution: comp.ai.neural-nets Organization: Cambridge University Engineering Department, UK Keywords: In article schultz@halley.est.3m.com (John C. Schultz) writes: # #Background: Given a set of experimental data where the researcher varied # the set of input paramters and measured the quality of the output. # #Problem: How to recommend new input control parameters which would # result in "BETTER" output(s). # #My (non-optimal) Solution: # #I trained a back-prop network on the existing data, attempting to accurately #model the "N dimensioanl response surface" of the experimental data. I can #then twiddle the input parameters to the trained network about the #experimental optimum(s). Using these simulated experiments I can then look for #improved output(s) and recommend new experimental settings. # #This approach is very crude, particularly for large numbers of input #variables. Does anyone have suggestions on ways to improve the search #efficiency? As you know that you can start near a good solution, why not back-propagate the errors back to the input of your network. By keeping the weights fixed, these errors can be used to do gradient decent in the input space which is a reasonable search strategy as locally the space should be fairly simple. Several people have suggested this technique, one good reference is [1], what is the experience of the net in using it in practice? Tony Robinson [1] A Linden and J Kindermann, "Inversion of Multilayer Nets", pp II425-II430, Proceedings of the INternational Joint Conference on Neural Networks, Washington DC, June 18-22, 1989.