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Type-1 and interval type-2 ANFIS: a comparison

Chen, Chao; John, Robert; Twycross, Jamie; Garibaldi, Jonathan M.

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Authors

CHAO CHEN Chao.Chen@nottingham.ac.uk
Transitional Assistant Professor

Robert John

Jonathan M. Garibaldi



Abstract

In a previous paper, we proposed an extended ANFIS architecture and showed that interval type-2 ANFIS produced larger errors than type-1 ANFIS on the well-known IRIS classification problem. In this paper, more experiments on both synthetic and real-world data are conducted to further investigate and compare the performance of interval type-2 ANFIS and type-1 ANFIS. For each dataset, interval type-2 ANFIS is optimised in three different ways, including a strategy suggested by Mendel such that interval type-2 ANFIS would be no worse than type-1 ANFIS. Our results show that in some circumstances the performance of interval type-2 ANFIS can be improved when it is initialised with blurred optimised type-1 ANFIS parameters. However, in general, interval type-2 ANFIS does not produce a clear performance improvement compared to type-1 ANFIS, especially on Mackey-Glass data with large noise. Thus, we conclude that the choice of interval type-2 ANFIS over type-1 ANFIS should be carefully considered, since type-2 ANFIS is more computationally complex, yet significantly better performance cannot be easily obtained.

Conference Name 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017)
End Date Jul 12, 2017
Acceptance Date Mar 14, 2017
Publication Date Aug 24, 2017
Deposit Date Apr 7, 2017
Publicly Available Date Aug 24, 2017
Electronic ISSN 1544-5615
Peer Reviewed Peer Reviewed
Public URL https://nottingham-repository.worktribe.com/output/878736
Publisher URL http://ieeexplore.ieee.org/abstract/document/8015555/
Related Public URLs https://www.fuzzieee2017.org/
Additional Information ISSN 1544-5615. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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