Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!ames!ucsd!sdcsvax!beowulf!biafore From: biafore@beowulf.ucsd.edu (Louis Steven Biafore) Newsgroups: comp.ai.neural-nets Subject: Tech report available Keywords: backpropagation, dynamic node creation Message-ID: <6072@sdcsvax.UCSD.Edu> Date: 7 Mar 89 20:43:19 GMT Sender: nobody@sdcsvax.UCSD.Edu Reply-To: biafore%cs.UCSD.EDU Distribution: na Organization: Computer Science & Engineering Dept. U.C. San Diego Lines: 46 ----------------------------------------------------------------------- The following technical report is now available. ----------------------------------------------------------------------- DYNAMIC NODE CREATION IN BACKPROPAGATION NETWORKS Timur Ash ash@ucsd.edu Abstract Large backpropagation (BP) networks are very difficult to train. This fact complicates the process of iteratively testing different sized networks (i.e., networks with dif- ferent numbers of hidden layer units) to find one that pro- vides a good mapping approximation. This paper introduces a new method called Dynamic Node Creation (DNC) that attacks both of these issues (training large networks and testing networks with different numbers of hidden layer units). DNC sequentially adds nodes one at a time to the hidden layer(s) of the network until the desired approximation accuracy is achieved. Simulation results for parity, symmetry, binary addition, and the encoder problem are presented. The pro- cedure was capable of finding known minimal topologies in many cases, and was always within three nodes of the minimum. Computational expense for finding the solutions was comparable to training normal BP networks with the same final topologies. Starting out with fewer nodes than needed to solve the problem actually seems to help find a solution. The method yielded a solution for every problem tried. BP applied to the same large networks with randomized initial weights was unable, after repeated attempts, to replicate some minimum solutions found by DNC. ----------------------------------------------------------------------- Requests for reprints should be sent to the Institute for Cognitive Science, C-015; University of California, San Diego; La Jolla, CA 92093. (ICS Report 8901) -----------------------------------------------------------------------