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)


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
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


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

ASJC Scopus subject areas

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

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