A flexible enhanced fuzzy min-max neural network for pattern classification

A flexible enhanced fuzzy min-max neural network for pattern classification

In this paper, the existing enhanced fuzzy min–max (EFMM) neural network is improved with a flexible learning procedure for undertaking pattern classification tasks. Four new contributions are introduced. Firstly, a new training strategy is proposed for avoiding the generation of unnecessary overlap...

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Bibliographic Details
Main Authors: Alhroob, Essam, Mohammed, Mohammed Falah, Al Sayaydeh, Osama Nayel, Hujainah, Fadhl, Ngahzaifa, Ab Ghani, Lim, Chee Peng
Format: Article
Language:English
English
Published: Elsevier 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/41060/1/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network_ABST.pdf
http://umpir.ump.edu.my/id/eprint/41060/2/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network%20for%20pattern%20classification.pdf

Internet

http://umpir.ump.edu.my/id/eprint/41060/1/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network_ABST.pdf
http://umpir.ump.edu.my/id/eprint/41060/2/A%20flexible%20enhanced%20fuzzy%20min-max%20neural%20network%20for%20pattern%20classification.pdf

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