Improvement of Fuzzy ARTMAP Performance in Noisy Input Environment Using Weighted-Average Learning
Summary :
An effective learning method for the fuzzy ARTMAP in the recognition of noisy input patterns is presented. the weight vectors of the system are updated using the weighted average of the noisy input vector and the weight vector itself. This method leads to stable learning and prevents the excessive update of the weight vectors which may cause performance degradation. Simulation results show that the proposed method not only reduces the generation of spurious categories, but aloso increases the recognition ratio in the noisy environment.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.5 pp.932-935
- Publication Date
- 1997/05/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Neural Networks
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Jae Sul LEE, Chang Joo LEE, Choong Woong LEE, "Improvement of Fuzzy ARTMAP Performance in Noisy Input Environment Using Weighted-Average Learning" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 5, pp. 932-935, May 1997, doi: .
Abstract: An effective learning method for the fuzzy ARTMAP in the recognition of noisy input patterns is presented. the weight vectors of the system are updated using the weighted average of the noisy input vector and the weight vector itself. This method leads to stable learning and prevents the excessive update of the weight vectors which may cause performance degradation. Simulation results show that the proposed method not only reduces the generation of spurious categories, but aloso increases the recognition ratio in the noisy environment.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_5_932/_p
Copy
@ARTICLE{e80-a_5_932,
author={Jae Sul LEE, Chang Joo LEE, Choong Woong LEE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improvement of Fuzzy ARTMAP Performance in Noisy Input Environment Using Weighted-Average Learning},
year={1997},
volume={E80-A},
number={5},
pages={932-935},
abstract={An effective learning method for the fuzzy ARTMAP in the recognition of noisy input patterns is presented. the weight vectors of the system are updated using the weighted average of the noisy input vector and the weight vector itself. This method leads to stable learning and prevents the excessive update of the weight vectors which may cause performance degradation. Simulation results show that the proposed method not only reduces the generation of spurious categories, but aloso increases the recognition ratio in the noisy environment.},
keywords={},
doi={},
ISSN={},
month={May},}
Copy
TY - JOUR
TI - Improvement of Fuzzy ARTMAP Performance in Noisy Input Environment Using Weighted-Average Learning
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 932
EP - 935
AU - Jae Sul LEE
AU - Chang Joo LEE
AU - Choong Woong LEE
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 1997
AB - An effective learning method for the fuzzy ARTMAP in the recognition of noisy input patterns is presented. the weight vectors of the system are updated using the weighted average of the noisy input vector and the weight vector itself. This method leads to stable learning and prevents the excessive update of the weight vectors which may cause performance degradation. Simulation results show that the proposed method not only reduces the generation of spurious categories, but aloso increases the recognition ratio in the noisy environment.
ER -
English
