Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth Model
This study addresses the fuzzy parameters (coefficient) determination for the logistic population growth model, proposing a novel methodology based on fuzzy logic concepts. Population dynamics are often modeled using differential equations whose parameters represent critical ecological information,...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/14/1/36 |
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| author | Yuney Gorrin-Ortega Selene Lilette Cardenas-Maciel Jorge Antonio Lopez-Renteria Nohe Ramon Cazarez-Castro |
| author_facet | Yuney Gorrin-Ortega Selene Lilette Cardenas-Maciel Jorge Antonio Lopez-Renteria Nohe Ramon Cazarez-Castro |
| author_sort | Yuney Gorrin-Ortega |
| collection | DOAJ |
| description | This study addresses the fuzzy parameters (coefficient) determination for the logistic population growth model, proposing a novel methodology based on fuzzy logic concepts. Population dynamics are often modeled using differential equations whose parameters represent critical ecological information, where the parameters determination is a problem itself. Unlike those approaches, the proposed methodology leverages ecosystem variables as inputs to a fuzzy inference system, which then generates fuzzy coefficients that better capture the inherent uncertainties in population dynamics. The approach was tested on a case study involving marine fish populations, where the fuzzy coefficients for growth rate and carrying capacity were calculated and integrated into the logistic model. The results illustrate that the fuzzy model with the proposed coefficients provide a robust framework for modeling population growth, preserving the increasing trajectory of the population under different scenarios. This method allows for the incorporation of expert knowledge and linguistic variables into the model, offering a more flexible and accurate representation of real-world ecosystems. The study concludes that this methodology significantly enhances the model’s applicability and predictive power, particularly in situations where precise data are not available. |
| format | Article |
| id | doaj-art-bfe7765fd2cf4647b09b8e038f76e767 |
| institution | Kabale University |
| issn | 2075-1680 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj-art-bfe7765fd2cf4647b09b8e038f76e7672025-01-24T13:22:13ZengMDPI AGAxioms2075-16802025-01-011413610.3390/axioms14010036Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth ModelYuney Gorrin-Ortega0Selene Lilette Cardenas-Maciel1Jorge Antonio Lopez-Renteria2Nohe Ramon Cazarez-Castro3Instituto Tecnologico de Tijuana, Tecnologico Nacional de Mexico, Tijuana 22414, BC, MexicoInstituto Tecnologico de Tijuana, Tecnologico Nacional de Mexico, Tijuana 22414, BC, MexicoCONAHCYT—Tecnológico Nacional de México/Instituto Tecnológico de Tijuana, Tijuana 22414, BC, MexicoInstituto Tecnologico de Tijuana, Tecnologico Nacional de Mexico, Tijuana 22414, BC, MexicoThis study addresses the fuzzy parameters (coefficient) determination for the logistic population growth model, proposing a novel methodology based on fuzzy logic concepts. Population dynamics are often modeled using differential equations whose parameters represent critical ecological information, where the parameters determination is a problem itself. Unlike those approaches, the proposed methodology leverages ecosystem variables as inputs to a fuzzy inference system, which then generates fuzzy coefficients that better capture the inherent uncertainties in population dynamics. The approach was tested on a case study involving marine fish populations, where the fuzzy coefficients for growth rate and carrying capacity were calculated and integrated into the logistic model. The results illustrate that the fuzzy model with the proposed coefficients provide a robust framework for modeling population growth, preserving the increasing trajectory of the population under different scenarios. This method allows for the incorporation of expert knowledge and linguistic variables into the model, offering a more flexible and accurate representation of real-world ecosystems. The study concludes that this methodology significantly enhances the model’s applicability and predictive power, particularly in situations where precise data are not available.https://www.mdpi.com/2075-1680/14/1/36fuzzy differential equationsverhulst modelfuzzy coefficientspopulation dynamicsexpert knowledge |
| spellingShingle | Yuney Gorrin-Ortega Selene Lilette Cardenas-Maciel Jorge Antonio Lopez-Renteria Nohe Ramon Cazarez-Castro Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth Model Axioms fuzzy differential equations verhulst model fuzzy coefficients population dynamics expert knowledge |
| title | Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth Model |
| title_full | Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth Model |
| title_fullStr | Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth Model |
| title_full_unstemmed | Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth Model |
| title_short | Parameters Determination via Fuzzy Inference Systems for the Logistic Populations Growth Model |
| title_sort | parameters determination via fuzzy inference systems for the logistic populations growth model |
| topic | fuzzy differential equations verhulst model fuzzy coefficients population dynamics expert knowledge |
| url | https://www.mdpi.com/2075-1680/14/1/36 |
| work_keys_str_mv | AT yuneygorrinortega parametersdeterminationviafuzzyinferencesystemsforthelogisticpopulationsgrowthmodel AT selenelilettecardenasmaciel parametersdeterminationviafuzzyinferencesystemsforthelogisticpopulationsgrowthmodel AT jorgeantoniolopezrenteria parametersdeterminationviafuzzyinferencesystemsforthelogisticpopulationsgrowthmodel AT noheramoncazarezcastro parametersdeterminationviafuzzyinferencesystemsforthelogisticpopulationsgrowthmodel |