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 language | English |
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Title of host publication | IEEE International Conference on Fuzzy Systems |
Publisher | IEEE |
Pages | 1253-1257 |
Number of pages | 5 |
Publication status | Published - 1 Jan 1998 |
Event | Proceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2) - Anchorage, AK, United States Duration: 4 May 1998 → 9 May 1998 |
Conference
Conference | Proceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2) |
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Country/Territory | United States |
City | Anchorage, AK |
Period | 4/05/98 → 9/05/98 |
ASJC Scopus subject areas
- Chemical Health and Safety
- Software
- Safety, Risk, Reliability and Quality