Interval Type-2 Fuzzy Membership Function Design and its Application to Radial Basis Function Neural Networks
- Title
- Interval Type-2 Fuzzy Membership Function Design and its Application to Radial Basis Function Neural Networks
- Author
- 이정훈
- Issue Date
- 2007-07
- Publisher
- IEEE
- Citation
- 2007 IEEE International Fuzzy Systems Conference, Page. 1-6
- Abstract
- Type-2 fuzzy sets has been shown to manage
uncertainty more effectively than type-1 fuzzy sets in several
pattern recognition applications [1]-[10]. However, computing
with type-2 fuzzy sets can require high computational
complexity since it involves numerous embedded type-2 fuzzy
sets. To reduce the complexity, interval type-2 fuzzy sets can be
used. In this paper, an interval type-2 fuzzy membership design
method and its application to radial basis function (RBF) neural
networks is proposed. Type-1 fuzzy memberships which are
computed from the centroid of the interval type-2 fuzzy
memberships are incorporated into the RBF neural network.
The proposed membership assignment is shown to improve the
classification performance of the RBF neural network since the
uncertainty of pattern data are desirably controlled by interval
type-2 fuzzy memberships. Experimental results for several
data sets are given.
- URI
- https://ieeexplore.ieee.org/document/4295680https://repository.hanyang.ac.kr/handle/20.500.11754/106675
- ISBN
- 1-4244-1209-9
- ISSN
- 1098-7584
- DOI
- 10.1109/FUZZY.2007.4295680
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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