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

Abstract This paper is the first of a two part series that reviews and critiques several identification algorithms for fuzzy relational matrices. Part 1 reviews and evaluates algorithms that do not optimize or minimize a specified performance criteria [3,9,20,24]. It compliments and extends a recent comparative identification analysis by Postlethwaite [17]. Part 2 [1] evaluates algorithms 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 algorithm with an on-line formulation that guarantees the completeness of the relational matrix, if sufficient 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.