CHAO CHEN Chao.Chen@nottingham.ac.uk
Transitional Assistant Professor
An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models
Chen, Chao; John, Robert; Twycross, Jamie; Garibaldi, Jonathan M.
Authors
Robert John
JAMIE TWYCROSS JAMIE.TWYCROSS@NOTTINGHAM.AC.UK
Associate Professor
Jonathan M. Garibaldi
Abstract
In this paper, an extended ANFIS architecture is proposed. By incorporating an extra layer for the fuzzification process, the extended architecture is able to fit both type-1 and interval type-2 models. The learning properties of the proposed architecture based on the least-squares estimate method are studied on selected type-1 and interval type-2 ANFIS models. We show that the least-squares estimate method in general behaves differently for interval type-2 ANFIS models compared to type-1 ANFIS models, producing larger errors for interval type-2 ANFIS.
Citation
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. (2016). An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models.
Conference Name | 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016) |
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End Date | Jul 29, 2016 |
Acceptance Date | Mar 14, 2016 |
Publication Date | Jul 29, 2016 |
Deposit Date | May 23, 2016 |
Publicly Available Date | Jul 29, 2016 |
Peer Reviewed | Peer Reviewed |
Public URL | https://nottingham-repository.worktribe.com/output/798428 |
Files
An.Extended.ANFIS.Architecture.pdf
(164 Kb)
PDF
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