A refined fuzzy min–max neural network with new learning procedures for pattern classification

Sayaydeh, Osama Nayel Al, Mohammed, Mohammed Falah, Alhroob, Essam, Tao, Hai and Lim, Chee Peng 2020, A refined fuzzy min–max neural network with new learning procedures for pattern classification, IEEE transactions on fuzzy systems, vol. 28, no. 10, pp. 2480-2494, doi: 10.1109/tfuzz.2019.2939975.

Attached Files
Name Description MIMEType Size Downloads

Title A refined fuzzy min–max neural network with new learning procedures for pattern classification
Author(s) Sayaydeh, Osama Nayel Al
Mohammed, Mohammed Falah
Alhroob, Essam
Tao, Hai
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Journal name IEEE transactions on fuzzy systems
Volume number 28
Issue number 10
Start page 2480
End page 2494
Total pages 15
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2020-10
ISSN 1063-6706
1941-0034
Keyword(s) fuzzy min–max model
hyperbox structure
neural network learning
online learning
pattern classification
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
Fuzzy neural networks
Distortion
Fuzzy logic
Neurons
Artificial neural networks
Fuzzy min-max model
Language eng
DOI 10.1109/tfuzz.2019.2939975
Indigenous content off
Field of Research 0102 Applied Mathematics
0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144428

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 14 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Mon, 26 Oct 2020, 07:11:47 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.