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

This paper is the first of a two part series that reviews and critiques sev eral identification algorithms for fuzzy relational matrices. Part 1 review s and evaluates algorithms that do not optimize or minimize a specified per formance criteria [3,9,20,24]. It compliments and extends a recent comparat ive identification analysis by Postlethwaite [17]. Part 2 [1] evaluates alg orithms that optimize or minimize a specified performance criteria [6,8,23, 26]. The relational matrix, learned by each algorithm from the Box-Jenkins gas furnace data [2], is compared for effectiveness of the prediction based on a minimum distance from actual. A new, non-optimized identification alg orithm with an on-line formulation that guarantees the completeness of the relational matrix, if sufficient learning has taken place, is also presente d. Results show that the proposed new algorithm ranks as the best among the non-optimized algorithms with prediction results very close to the optimiz ation methods of Part 2. (C) 2000 Elsevier Science B.V. All rights reserved .