Forward and backward fuzzy rule base interpolation using fuzzy geometry
Fuzzy rule interpolation (FRI) predicts an accountable outcome of a possible course of action in sparse fuzzy rule base system (FRBS). The geometry based linear fuzzy rule interpolation (GLFRI) is extended for multi-dimensional fuzzy rule base interpolation. Expansion/contraction (EC) of triangular,...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/173620 |
| _version_ | 1826122179092152320 |
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| author | Das, Suman Chakraborty, Debjani Kóczy, László T. |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Das, Suman Chakraborty, Debjani Kóczy, László T. |
| author_sort | Das, Suman |
| collection | NTU |
| description | Fuzzy rule interpolation (FRI) predicts an accountable outcome of a possible course of action in sparse fuzzy rule base system (FRBS). The geometry based linear fuzzy rule interpolation (GLFRI) is extended for multi-dimensional fuzzy rule base interpolation. Expansion/contraction (EC) of triangular, trapezoidal and complex polygonal fuzzy sets has been also proposed which enables the proposed FRI method to incorporate with fuzzy rules which include triangular, trapezoidal, hexagonal or complex fuzzy sets. The study further extends to introduce the process of backward rule base interpolation. It has been shown that the scale and move transformation-based FRI method can yield a non-convex fuzzy consequent which can be avoided by using the proposed method. The proposed method performs better without any risk of obtaining non-convex fuzzy consequent. The efficiency of proposed forward and backward FRI methods is projected with several numerical examples. A detailed comparison of EC transformation with scale and move transformation is also presented here. |
| first_indexed | 2024-10-01T05:44:15Z |
| format | Journal Article |
| id | ntu-10356/173620 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:44:15Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1736202024-02-23T15:35:53Z Forward and backward fuzzy rule base interpolation using fuzzy geometry Das, Suman Chakraborty, Debjani Kóczy, László T. School of Computer Science and Engineering Computer and Information Science Inverse rule base interpolation Scale and move transformation Fuzzy rule interpolation (FRI) predicts an accountable outcome of a possible course of action in sparse fuzzy rule base system (FRBS). The geometry based linear fuzzy rule interpolation (GLFRI) is extended for multi-dimensional fuzzy rule base interpolation. Expansion/contraction (EC) of triangular, trapezoidal and complex polygonal fuzzy sets has been also proposed which enables the proposed FRI method to incorporate with fuzzy rules which include triangular, trapezoidal, hexagonal or complex fuzzy sets. The study further extends to introduce the process of backward rule base interpolation. It has been shown that the scale and move transformation-based FRI method can yield a non-convex fuzzy consequent which can be avoided by using the proposed method. The proposed method performs better without any risk of obtaining non-convex fuzzy consequent. The efficiency of proposed forward and backward FRI methods is projected with several numerical examples. A detailed comparison of EC transformation with scale and move transformation is also presented here. Ministry of Education (MOE) Published version The first author acknowledges the support given by MoE, Singapore, Tier-2 grant number MOE2019-T2-2-040. The third author acknowledges the support given by National Research, Development and Innovation Office (NKFIH), grant no. K108405. 2024-02-19T06:36:51Z 2024-02-19T06:36:51Z 2023 Journal Article Das, S., Chakraborty, D. & Kóczy, L. T. (2023). Forward and backward fuzzy rule base interpolation using fuzzy geometry. Iranian Journal of Fuzzy Systems, 20(3), 127-146. https://dx.doi.org/10.22111/ijfs.2023.7643 1735-0654 https://hdl.handle.net/10356/173620 10.22111/ijfs.2023.7643 2-s2.0-85160301260 3 20 127 146 en MOE2019-T2-2-040 Iranian Journal of Fuzzy Systems © Iranian Journal of Fuzzy Systems. This is an open-access article distributed under the terms of the Creative Commons License. application/pdf |
| spellingShingle | Computer and Information Science Inverse rule base interpolation Scale and move transformation Das, Suman Chakraborty, Debjani Kóczy, László T. Forward and backward fuzzy rule base interpolation using fuzzy geometry |
| title | Forward and backward fuzzy rule base interpolation using fuzzy geometry |
| title_full | Forward and backward fuzzy rule base interpolation using fuzzy geometry |
| title_fullStr | Forward and backward fuzzy rule base interpolation using fuzzy geometry |
| title_full_unstemmed | Forward and backward fuzzy rule base interpolation using fuzzy geometry |
| title_short | Forward and backward fuzzy rule base interpolation using fuzzy geometry |
| title_sort | forward and backward fuzzy rule base interpolation using fuzzy geometry |
| topic | Computer and Information Science Inverse rule base interpolation Scale and move transformation |
| url | https://hdl.handle.net/10356/173620 |
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