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

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

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.

Citation

Chen, C., John, R., Twycross, J., & Garibaldi, J. M. Type-1 and interval type-2 ANFIS: a comparison. Presented at 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017)

• 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/
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• Contract Date: Apr 7, 2017