Fuzzy regression analysis using RFLN and its application

Xinxue Zhang, Shinichiro Omachi, Hirotomo Aso

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)

Abstract

When we attempt to model a complex system including human as an important component, it may be difficult to represent the system by a deterministic mathematical model. The main reason of this difficulty is that the system itself inherently has some fuzziness concerning subjective judgement of human. In this paper, we propose a fuzzy nonlinear regression method with RFLN (RCE-based Fuzzy Learning Network), which is capable of extracting knowledge of the experts automatically. RFLN is an extended RCE (Restricted Coulomb Energy) model, hence it needs few iterations in learning and its additional learning is easy. The proposed method has higher flexibility than fuzzy linear regression models. We propose learning algorithms to identify a nonlinear interval model which approximately includes all the given input-output data. The proposed method has characteristics of faster learning and of easier additional learning. The effectiveness of the method is shown by numerical experiments.

Original languageEnglish
Pages51-56
Number of pages6
Publication statusPublished - 1997 Jan 1
EventProceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3) - Barcelona, Spain
Duration: 1997 Jul 11997 Jul 5

Other

OtherProceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3)
CityBarcelona, Spain
Period97/7/197/7/5

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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