An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models
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 a...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2016
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| Online Access: | https://eprints.nottingham.ac.uk/33465/ |
| _version_ | 1848794635862802432 |
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| author | Chen, Chao John, Robert Twycross, Jamie Garibaldi, Jonathan M. |
| author_facet | Chen, Chao John, Robert Twycross, Jamie Garibaldi, Jonathan M. |
| author_sort | Chen, Chao |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:19:20Z |
| format | Conference or Workshop Item |
| id | nottingham-33465 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:19:20Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-334652020-05-04T17:58:59Z https://eprints.nottingham.ac.uk/33465/ 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. 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. 2016-07-29 Conference or Workshop Item PeerReviewed 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. |
| spellingShingle | Chen, Chao John, Robert Twycross, Jamie Garibaldi, Jonathan M. An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models |
| title | An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models |
| title_full | An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models |
| title_fullStr | An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models |
| title_full_unstemmed | An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models |
| title_short | An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models |
| title_sort | extended anfis architecture and its learning properties for type-1 and interval type-2 models |
| url | https://eprints.nottingham.ac.uk/33465/ |