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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 robert.john@nottingham.ac.uk

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)
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 http://eprints.nottingham.ac.uk/id/eprint/33465
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf

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An.Extended.ANFIS.Architecture.pdf (164 Kb)
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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