An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models

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Chen, Chao, John, Robert, Twycross, Jamie and Garibaldi, Jonathan M. (2016) An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), 24-29 July 2016, Vancouver, Canada.

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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.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/798428
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: John, Professor Robert
Date Deposited: 23 May 2016 14:57
Last Modified: 04 May 2020 17:58
URI: https://eprints.nottingham.ac.uk/id/eprint/33465

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