MISO intuitionistic fuzzy inference system
Fuzzy Inference Systems (FIS) are widely used for decision-making through imprecise relation defined on qualitative imprecise inputs and outputs based on expert’s knowledge via fuzzy numbers. Even the fuzzy relation is based on the expert’s knowledge, it is not accounted with the expert’s lack of co...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2025-03-01
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| Series: | Journal of Fuzzy Extension and Applications |
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| Online Access: | https://www.journal-fea.com/article_209458_2bb3dfe0c880b620da3439bda7358f98.pdf |
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| _version_ | 1832577753352765440 |
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| author | Lakshmana Nayagam Velu Daniel Paulraj |
| author_facet | Lakshmana Nayagam Velu Daniel Paulraj |
| author_sort | Lakshmana Nayagam Velu |
| collection | DOAJ |
| description | Fuzzy Inference Systems (FIS) are widely used for decision-making through imprecise relation defined on qualitative imprecise inputs and outputs based on expert’s knowledge via fuzzy numbers. Even the fuzzy relation is based on the expert’s knowledge, it is not accounted with the expert’s lack of confidence / hesitancy, if any, involved in the relation between qualitative imprecise inputs and outputs because of their imprecise / fuzzy in nature. This research introduces an enhanced Intuitionistic Fuzzy Inference System (IFIS) to overcome the limitations of conventional Fuzzy Inference Systems (FIS) in handling imprecise, incomplete, and uncertain expert’s data by considering expert’s hesitancy / lack of knowledge in domain. IFIS extends traditional fuzzy models by incorporating intuitionistic fuzzification, intuitionistic IF-THEN implications, and intuitionistic defuzzification, all of which account for expert’s hesitation / lack of confidence if any due to lack of knowledge through an α - level hesitancy parameter. A Multi-Input Single-Output (MISO) intuitionistic fuzzy system is developed as a generalization of the Single-Input Single-Output (SISO) model. To demonstrate the utility of this approach, the study applies a trapezoidal intuitionistic fuzzy inference system (TIFIS) to model COVID-19 risk, assuming expert data may exhibit varying degrees of confidence. This novel framework significantly enhances decision-making processes in complex, uncertain environments, offering a robust alternative to existing FIS models. |
| format | Article |
| id | doaj-art-87070bb73ce7428c833aac2bdc60e587 |
| institution | Kabale University |
| issn | 2783-1442 2717-3453 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | Journal of Fuzzy Extension and Applications |
| spelling | doaj-art-87070bb73ce7428c833aac2bdc60e5872025-01-30T15:07:29ZengAyandegan Institute of Higher Education,Journal of Fuzzy Extension and Applications2783-14422717-34532025-03-016116218910.22105/jfea.2024.450225.1432209458MISO intuitionistic fuzzy inference systemLakshmana Nayagam Velu0Daniel Paulraj1Department of Mathematics (DST-FIST Sponsored), National Institute of Technology, Tiruchirappalli, India.Department of Mathematics, St. Xavier’s College (Autonomous), Palayamkiottai, Tirunelveli, Tamil Nadu, India.Fuzzy Inference Systems (FIS) are widely used for decision-making through imprecise relation defined on qualitative imprecise inputs and outputs based on expert’s knowledge via fuzzy numbers. Even the fuzzy relation is based on the expert’s knowledge, it is not accounted with the expert’s lack of confidence / hesitancy, if any, involved in the relation between qualitative imprecise inputs and outputs because of their imprecise / fuzzy in nature. This research introduces an enhanced Intuitionistic Fuzzy Inference System (IFIS) to overcome the limitations of conventional Fuzzy Inference Systems (FIS) in handling imprecise, incomplete, and uncertain expert’s data by considering expert’s hesitancy / lack of knowledge in domain. IFIS extends traditional fuzzy models by incorporating intuitionistic fuzzification, intuitionistic IF-THEN implications, and intuitionistic defuzzification, all of which account for expert’s hesitation / lack of confidence if any due to lack of knowledge through an α - level hesitancy parameter. A Multi-Input Single-Output (MISO) intuitionistic fuzzy system is developed as a generalization of the Single-Input Single-Output (SISO) model. To demonstrate the utility of this approach, the study applies a trapezoidal intuitionistic fuzzy inference system (TIFIS) to model COVID-19 risk, assuming expert data may exhibit varying degrees of confidence. This novel framework significantly enhances decision-making processes in complex, uncertain environments, offering a robust alternative to existing FIS models.https://www.journal-fea.com/article_209458_2bb3dfe0c880b620da3439bda7358f98.pdfmiso intuitionistic fuzzy inference mechanismsintuitionistic defuzzificationtrapezoidal intuitionistic fuzzification with hesitancycovid-19 intuitionistic fuzzy model |
| spellingShingle | Lakshmana Nayagam Velu Daniel Paulraj MISO intuitionistic fuzzy inference system Journal of Fuzzy Extension and Applications miso intuitionistic fuzzy inference mechanisms intuitionistic defuzzification trapezoidal intuitionistic fuzzification with hesitancy covid-19 intuitionistic fuzzy model |
| title | MISO intuitionistic fuzzy inference system |
| title_full | MISO intuitionistic fuzzy inference system |
| title_fullStr | MISO intuitionistic fuzzy inference system |
| title_full_unstemmed | MISO intuitionistic fuzzy inference system |
| title_short | MISO intuitionistic fuzzy inference system |
| title_sort | miso intuitionistic fuzzy inference system |
| topic | miso intuitionistic fuzzy inference mechanisms intuitionistic defuzzification trapezoidal intuitionistic fuzzification with hesitancy covid-19 intuitionistic fuzzy model |
| url | https://www.journal-fea.com/article_209458_2bb3dfe0c880b620da3439bda7358f98.pdf |
| work_keys_str_mv | AT lakshmananayagamvelu misointuitionisticfuzzyinferencesystem AT danielpaulraj misointuitionisticfuzzyinferencesystem |