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.

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. An extended ANFIS architecture and its learning properties for type-1 and interval type-2 models. Presented at 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016)

• 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: https://nottingham-repository.worktribe.com/output/798428
• Contract Date: May 23, 2016