Title

Parallelization Of Fuzzy Artmap To Improve Its Convergence Speed: The Network Partitioning Approach And The Data Partitioning Approach

Keywords

BEOWULF parallel processing; Data partitioning; Fuzzy ARTMAP; Network partitioning

Abstract

One of the properties of FAM, which can be both an asset and a liability, is its capacity to produce new neurons (templates) on demand to represent classification categories. This property allows FAM to automatically adapt to the database without having to arbitrarily specify network structure. We provide two methods for speeding up the FAM algorithm. The first one, referred to as the data partitioning approach, partitions the data into subsets for independent processing. The second one, referred to as the network partitioning approach, uses a pipeline to distribute the work between processes during training. We provide experimental results on a Beowulf cluster of workstations for both approaches that confirm the speedup of the modifications. © 2005 Elsevier Ltd. All rights reserved.

Publication Date

11-30-2005

Publication Title

Nonlinear Analysis, Theory, Methods and Applications

Volume

63

Issue

5-7

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.na.2005.02.013

Socpus ID

28044436727 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/28044436727

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