| Issue № 3 |
Methods of ecological investigations |
pdf-version |
Solving the problem of classification using fuzzy logic
| Korosov Andrey Victorovich | DSc, professor, Petrozavodsk State University, Petrozavodsk, Lenin st., 33, korosov@psu.karelia.ru |
|
Keywords: fuzzy logic membership function gender diagnosis common viper |
Summary: The work is devoted to the use of fuzzy logic methods to solve problems of classification of biological objects. The construction and use of fuzzy inference by the Zadeh – Mamdani method is considered. The task is to develop logical rules for determining the sex of a viper in life according to a series of qualitative and quantitative morphological features. Two variants of solving the problem: using formal binary logic methods and using methods of fuzzy logic are shown. The main components of formal logic (concepts, judgments, laws) and fuzzy logic (membership functions, linguistic variables, calculation of fuzzy inferences) are considered. The methods of defining membership functions are indicated; the method of composing logistic equations using distributions of feature values over an extensive sample of animals is implemented. It is proposed to interpret membership functions as a semantic filter that enhances the diagnostic properties of the studied features. All calculations are illustrated by numerical examples. For all stages of modeling, their own scripts are written, attached by hyperlink to the text. Executing scripts in the R package environment will allow getting acquainted with all the stages of the study in detail. The reasons that this method is not in demand in the practice of environmental research are discussed. The issues of parameter settings and verification methods of the logical model are considered. It is noted that the direction of integration of fuzzy logic with neural network modeling is actively developing. © Petrozavodsk State University |
Received on: 18 May 2024 Published on: 27 September 2024 | |
References
Acun F., Çunkaş M. Low-cost fuzzy logic-controlled home energy management system, Journal of Electrical Systems and Inf Technol. 2023. Vol. 10, No. 31. P. 1–20. DOI: 10.1186/s43067-023-00100-6
Anisimova E. S. Neuro-fuzzy networks, Ekonomika i socium. 2015. No. 3 (16). C. 33–36.
Asman T., Saleh H. M., Mohammed A. H. Obtaining unique by analyzing DNA using a neuro-fuzzy algorithm, Journal of University of Anbar for Pure Science. 2023. Vol. 17. P. 158–168. DOI: h10.37652/juaps.2023.178906
Baldi P., Brunak S. Bioinformatics: The Machine Learning Approach. Cambridge: Massachusetts Institute of Technology, 2001. 452 p.
Bandyopadhyay S., Maulik U., Wang J. T. L. Analysis of biological data: a soft computing approach., Science, Engineering, and Biology Informatics. Vol. 3 New Jersey: World Scientific Publishing Co. Pte. Ltd., 2007. 332 p.
Bykov A. V. Korenevskiy N. A. Ustinov A. G. Fuzzy algorithm for predicting the development of ischemic limb disease for various stages of patient management, Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tehnika, informatika. Medicinskoe priborostroenie. 2016. No. 2 (19). P. 142–155.
Chang M. Modern Issues and Methods in Biostatistics. New York: Springer Science + Business Media, 2011. 307 p.
Chernov V. G. Fuzzy sets. Fundamentals of theory and application: Ucheb. posobie. Vladimir: Izd-vo VlGU, 2018. 156 p.
Du E. Y. Biometrics. from fiction to practice. Boca raton: CRC Press, 2012. 299 p.
Dyuk V. Emanuel' V. Information technologies in biomedical research. SPb.: Piter, 2003. 528 p.
Dzheyms G. Uitton A. Hasti T. Tibshirani R. An introduction to statistical learning whith examples in R. M.: DMK Press, 2016. 450 p.
Grigor'eva D. R. Gareeva G. A. Basyrov R. R. Fundamentals of fuzzy logic: Educational and methodical manual for practical classes and laboratory work. Naberezhnye Chelny: Izd-vo NChI KFU, 2018. 42 p.
Har'kov C. B. Assessment of the postoperative condition of urological patients based on fuzzy models, Medicinskie pribory i tehnologii: Mezhdunarodnyy sbornik nauchnyh statey. Vyp. 4. Tula: TulGU, 2011. P. 258–260.
Korosov A. V. Ecology of the common viper (Vipera berus L.) in the North (facts and models). Petrozavodsk: Izd-vo PetrGU, 2010. 264 p.
Korosov A. V. Simulation modeling in MS Excel environment (using examples from ecology). Petrozavodsk, 2002. 212 p.
Lange F. Fuzzy logic. M.: Strata, 2018. 134 p. URL: https://online-biblio.tk/bookid_54688470/ (data obrascheniya: 15.04.2024).
Lubencova E. V. Piotrovskiy D. L. Research of learning algorithms for a neuro-fuzzy control system of a biotechnological process, Nauchnyy zhurnal KubGAU. 2017. No. 128 (04). P. 1–11.
MEPhI-2005 Scientific Session. VII All-Russian Scientific and Technical Conference "Neuroinformatics-2005": Lectures on neuroinformatics. M.: MIFI, 2005. 214 p.
Melin P., Carlos Guzman J., Prado Arechiga G. Neuro Fuzzy Hybrid Models for Classification in Medical Diagnosis. Springer, 2021. 109 p. DOI: 10.1007/978-3-030-60481-3
Menshutkin V. V. Classification of Karelian lakes using the apparatus of fuzzy logic, Iskusstvo modelirovaniya (ekologiya, fiziologiya, evolyuciya). Petrozavodsk; SPb., 2010. P. 190–198. URL: https://litmir.club/bd/?b=597749 (data obrascheniya: 15.04.2024).
Osovskiy S. Neural networks for information processing. M.: Finansy i statistika, 2002. 344 p.
Pagano M., Gauvreau K. Principles of Biostatistics. Boca Raton: CRC Press, 2018. 525 p.
Paklin N. Fuzzy logic – mathematical foundations, BaseGroup Labs. Tehnologii analiza dannyh. 2021. URL: https://loginom.ru/blog/fuzzy-logic (data obrascheniya: 15.04.2024).
Quinn G. P., Keough K. J. Experimental design and data analysis for biologists. Cambridge: Cambridge University Press, 2002. 520 r.
Ramirez-Mendoza R. A. Biometry. Technology, Trends and Applications, Ed. R. A. Ramirez-Mendoza et al. Monterrey: CRC Press, 2022. 218 p.
Rybin V. V. Fundamentals of the theory of fuzzy sets and fuzzy logic. M.: Izd-vo MAI, 2007. 96 p. URL: https://b.twirpx.link/file/635614/ (data obrascheniya: 15.04.2024).
Shitikov V. K. Rozenberg G. S. Randomization and bootstrap: statistical analysis in biology and ecology using R. Tol'yatti: Kassandra, 2013. 314 p.
Sokolov I. D. Sokolova E. I. Troshin L. P. Koltakov O. M. Naumov S. Yu. Medved' O. M. Introduction to biometrics: Ucheb. posobie. Krasnodar, 2016. 246 p.
Svincov V. I. Logic. M.: Vysshaya shkola, 1987. 287 p. URL: https://b.twirpx.link/file/2838072/ (data obrascheniya: 15.04.2024).
The R Project for Statistical Computing. 2023. URL: https://www.r-project.org/ (data obrascheniya: 26.07.2023).
Zade L. The concept of a linguistic variable and its application to making approximate decisions. M.: Mir, 1976. 167 p. URL: https://vk.com/topic-189923849_40476581 (data obrascheniya: 15.04.2024).
Zyubova N. I. Classification methods in the diagnosis of urolithiasis using fuzzy logic for data preprocessing, Informacionno-upravlyayuschie sistemy. 2013. No. 6. P. 85–90. URL: http://www.i-us.ru/index.php/ius/article/view/13841 (data obrascheniya: 15.04.2024).
© 2011 - 2024
Petrozavodsk State University
technical support - RCNIT
The editor-in-chief - Andrey Korosov
ecopri@petrsu.ru