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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/41060/ 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/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