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Identification algorithms for fuzzy relational matrices, Part 1: Non-optimizing algorithms

✍ Scribed by Mary M. Bourke; D. Grant Fisher

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
182 KB
Volume
109
Category
Article
ISSN
0165-0114

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✦ Synopsis

This paper is the ÿrst of a two part series that reviews and critiques several identiÿcation algorithms for fuzzy relational matrices. Part 1 reviews and evaluates algorithms that do not optimize or minimize a speciÿed performance criteria [3,9,20,24]. It compliments and extends a recent comparative identiÿcation analysis by Postlethwaite [17]. Part 2 [1] evaluates algorithms that optimize or minimize a speciÿed performance criteria [6,8,23,26]. The relational matrix, learned by each algorithm from the Box-Jenkins gas furnace data [2], is compared for e ectiveness of the prediction based on a minimum distance from actual. A new, non-optimized identiÿcation algorithm with an on-line formulation that guarantees the completeness of the relational matrix, if su cient learning has taken place, is also presented. Results show that the proposed new algorithm ranks as the best among the non-optimized algorithms with prediction results very close to the optimization methods of Part 2.

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