Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification
An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd
2017
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/16440/1/fskkp-2017-falah-Improving%20the%20fuzzy%20min-max1.pdf http://umpir.ump.edu.my/id/eprint/16440/ https://doi.org/10.1016/j.asoc.2016.12.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Malaysia Pahang |
| Language: | English |