Elsevier

Fuzzy Sets and Systems

Volume 42, Issue 3, 15 August 1991, Pages 315-334
Fuzzy Sets and Systems

Successive identification of a fuzzy model and its applications to prediction of a complex system

https://doi.org/10.1016/0165-0114(91)90110-CGet rights and content

Abstract

A successive identification method of a fuzzy model is suggested. The identification mechanism consists of two levels. One is the supervisor level and the other is the adjustment level The supervisor level determines a policy of parameter adjustment using a set of fuzzy adjustment rules. The adjustment rules are derived from the fuzzy implications of a fuzzy model and are extended to fuzzy adjustment rules by using an extended concept of Zadeh's contrast intensification. The adjustment level executes the policy of parameter adjustment determined with the fuzzy adjustment rules. The parameter adjustment consists of premise parameter adjustment and consequent parameter adjustment. Both of them are realized by the weighted recursive least square algorithm. Finally, it is shown from two examples that the method is very useful for modeling complex systems.

References (10)

There are more references available in the full text version of this article.

Cited by (357)

  • Target-scale prospectivity modeling for gold mineralization within the Rajapalot Au-Co project area in northern Fennoscandian Shield, Finland. Part 1: Application of knowledge-driven- and machine learning-based-hybrid- expert systems for exploration targeting and addressing model-based uncertainties

    2022, Ore Geology Reviews
    Citation Excerpt :

    This is also confirmed by the data statistics and data visualization plots (Appendix B). Prospectivity modeling was implemented using two types of rule-based Fuzzy Inference Systems (FISs): (1) knowledge-driven Mamdani type FIS (Mamdani, 1974; Mamdani and Assilian, 1975), and (2) hybrid Takagi-Sugeno (T-S) type (Sugeno and Kang, 1988; Sugeno and Tanaka, 1991; Takagi and Sugeno, 1985) adaptive neuro fuzzy inference system (ANFIS) (Jang, 1993). To address FIS-model related uncertainties, the FISs were optimized using – (i) the MCS-based statistical approach, implemented on a knowledge driven Mamdani-type FIS, and (ii) the machine-learning based approach, implemented using an adaptive neuro fuzzy inference system.

View all citing articles on Scopus
View full text