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/ |
| Summary: | 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. |
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