Output details
13 - Electrical and Electronic Engineering, Metallurgy and Materials
Queen's University Belfast
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Article title
A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models
Type
D - Journal article
Title of journal
IEEE Transactions on Fuzzy Systems
Article number
-
Volume number
21
Issue number
1
First page of article
30
ISSN of journal
1063-6706
Year of publication
2013
URL
-
Number of additional authors
2
Additional information
The majority of reported learning methods for Takagi-Sugeno-Kang(TSK) fuzzy neural models to date mainly focus on the improvement of model accuracy. How to find a desirable set of fuzzy partitions and, hence, identify the corresponding consequent models which can be directly explained in terms of system local behaviour, presents a critical step in fuzzy neural modelling. This EPSRC-funded work led to a new integrated gradient descent learning and the method is further supported by 2013 EPSRC project (EP/L001063/1) for developing new EV charging system, and also in the 2013 InvestNI Proof of Concept project Fuzzy controller for polymer extrusion.
Interdisciplinary
-
Cross-referral requested
-
Research group
C - Energy, Power and Intelligent Control (EPIC)
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
