"First, a brief overview of neural networks and their applications are described, including the BAM (Bidirectional Associative Memory) model.
A bucket-weight-matrix scheme is proposed, which is a data pattern encoding method that is necessary to transform a set of real-world numbers into neural network state numbers without losing the pattern property the set has. The scheme is designed as a neural net so that it can be combined with other data processing neural nets. The net itself can be used as a bucket-sorting net also. This shows that traditional data structure problems can be an area that neural networks may conquer, too.
A simulation of the net combined with the BAM model on a digital computer is done to show performance of the proposed data encoding method with both non-numerical image pattern and numerical data pattern examples"--Abstract, page iii.
Zobrist, George W. (George Winston), 1934-
Prater, John Bruce, 1932-2002
Bourquin, Jack J.
M.S. in Computer Science
University of Missouri--Rolla
ix, 72 pages
© 1990 Hyeoncheol Kim, All rights reserved.
Thesis - Restricted Access
Print OCLC #
Link to Catalog Record
Electronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.http://merlin.lib.umsystem.edu/record=b2296027~S5
Kim, Hyeoncheol, "Input data pattern encoding for neural net algorithms" (1990). Masters Theses. 911.
Share My Thesis If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the button above.