Identification algorithms for fuzzy relational matrices, Part 2: Optimizing algorithms

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

• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 207 KB
• Volume: 109
• Category: Article
• ISSN: 0165-0114
• DOI: 10.1016/s0165-0114(98)00302-9

✦ Synopsis

This paper, Part 2 of a two part series, reviews and evaluates four (4) algorithms that identify fuzzy relational matrices by optimizing a user-speciÿed performance index [6,8,29,34]. The performance of the Recursive Parameter method [34] was unsatisfactory but the Probabilistic Descent [6], Neural Learning [29] and Quasi-Newton [8] methods all gave comparable results that, in general, were better than the non-optimizing algorithms [3,9,23,32] reviewed in Part 1 [1]. However, the tuning and iteration required for these optimizing algorithms makes them less desirable for most on-line applications than the non-optimizing techniques of Shaw et al. [23] and Pedrycz [9]. It was also noted that results expressed in terms of fuzzy indices, Q q, were very poorly correlated (in fact tended to an inverse correlation) with results based on the non-fuzzy discrete indices. Jq preferred in many practical applications.