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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

Chao Chen

Robert John robert.john@nottingham.ac.uk

Jamie Twycross

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.

Publication Date Jul 29, 2016
Peer Reviewed Peer Reviewed
APA6 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
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../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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