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A CUDA‐streams inference machine for non‐singleton fuzzy systems
Author(s) -
TéllezVelázquez Arturo,
CruzBarbosa Raúl
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4382
Subject(s) - computer science , fuzzy set operations , fuzzy logic , fuzzy rule , singleton , artificial intelligence , data mining , fuzzy number , fuzzy control system , fuzzy set , pregnancy , biology , genetics
Summary The inferences computed on a non‐singleton fuzzy system represent a processing challenge when the number of linguistic variables and terms in a rule set are large, since they involve the use of a lot of sequential vector operations. To alleviate part of the inference process, a general‐purpose CUDA fuzzy tool for building non‐singleton fuzzy systems is presented in this paper. Here, we introduce an inference machine architecture that processes string‐based rules and concurrently executes them, based on an execution plan created by a fuzzy rule scheduler. This operation breaks down the n −ary fuzzy operations in several binary fuzzy operations which can be executed in several streams and stages. As a result, this approach lets a system both take advantage of the parallel nature of rule sets and achieve competitive speed‐up ratios without losing generality, when selecting large numbers of linguistic variables, linguistic terms, and rules. The object‐oriented nature of the proposed tool makes it an easy way to build fuzzy systems without having a deep knowledge of its architecture, as it is shown in the results of two case studies for testing the fuzzy system operation limit and detecting edges in digital images.