Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uunet!shelby!msi.umn.edu!cs.umn.edu!mmm.serc.3m.com!news From: schultz@halley.est.3m.com (John C. Schultz) Newsgroups: comp.ai.neural-nets Subject: optimization using neural networks Message-ID: Date: 9 Feb 91 22:16:06 GMT Sender: news@mmm.serc.3m.com Distribution: comp.ai.neural-nets Organization: 3M Company, 3M Center, Minnesota, USA Lines: 30 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? The literature I have found on optimization with neural networks seems to deal exclusively with the traveling salemen problem. However I don't think that the TSP is a good simulation for my situation since I do not have a cost function to move from one data point to the next. Thank you for any suggestions. -- John C. Schultz EMAIL: schultz@halley.serc.3m.com 3M Company, Building 518-01-1 WRK: +1 (612) 733-4047 1865 Woodlane Drive, Dock 4, Woodbury, MN 55125 How to include the taste of Glendronach in a multi-media system?