Mamdani type multistage fuzzy neural network model

Ji cheng Duan, Fu Lai Korris Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)

Abstract

In this paper, a new multistage fuzzy neural network model is proposed to overcome the dimensionality problem of single-stage fuzzy neural networks. The model arranges single-stage reasoning stages in a multistage manner, where the consequence of one stage can be passed to the next stage as a fact. The network structure in each individual stage is developed based on Lin and Lee's fuzzy neural network model in which Mamdani's fuzzy reasoning is adopted. Given the stipulated input-output data pairs, an appropriate fuzzy rule set can be created through a hybrid learning process. Simulation Results show that the new model uses less resources (e.g., fuzzy rules, t-norm and t-conorm operations) than its single-stage counterpart to achieve favorable performance. Some interesting results have also been found in convergence and robustness.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
PublisherIEEE
Pages1253-1257
Number of pages5
Publication statusPublished - 1 Jan 1998
EventProceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2) - Anchorage, AK, United States
Duration: 4 May 19989 May 1998

Conference

ConferenceProceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2)
Country/TerritoryUnited States
CityAnchorage, AK
Period4/05/989/05/98

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality

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