NDLI: Convergence and Optimization Study of a Growing Parallel Som Through a Genetic Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Beaton, D. Valova, I. MacLean, D. Hammond, J.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Massachusetts Univ., N. Dartmouth, MA (Beaton, D.; Valova, I.; MacLean, D.; Hammond, J.)
Abstract	A self-organizing map (SOM) is a type of unsupervised artificial neural network (ANN) that can be used in applications of pattern recognition, and classification. A SOM is a viable approach to many avionics problem domains that include threat identification (classification), air traffic flow management (pattern recognition) and intelligent systems for vehicle autonomy (classification and pattern recognition). An implementation of a parallelized SOM entitled ParaSOM, was developed which allows for a more accurate mapping of input; and far less iterations (hundreds, as opposed to tens or hundreds of thousands) are required in this implementation versus a classical SOM (Kohonen, 1995) and many of its variations - including but not limited to growing cell structures (Fritzke, 1994); growing grid (Fritzke, 1995); hierarchical (Lampinen and Oja, 1992); and growing hierarchical (Dittenbach et al., 2000). In a recent advancement to ParaSOM a genetic algorithm (GA) implementing evolutionary computation was created that quasi-randomly generates (or randomly selects) values for ParaSOM parameters from a lower and upper bound pairing of values for each ParaSOM parameter elected for use during execution. When used in conjunction with a convergence test, the GA identifies parameters of ParaSOM that bring execution and performance as close to optimum as possible, without human interaction. An automated generation of parameters for optimum performance of ParaSOM allows for a more accurate use of ParaSOM and therefore more accurate use in the problem domains discussed. Optimum performance is defined as highest accuracy of classification with least amount of iterations prior to convergence
Starting Page	1
Ending Page	9
File Size	405743
Page Count	9
File Format	PDF
ISBN	1424403774
DOI	10.1109/DASC.2006.313734
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Convergence Genetic algorithms Pattern recognition Artificial neural networks Aerospace electronics Telecommunication traffic Intelligent systems Artificial intelligence Intelligent vehicles Evolutionary computation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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