ПРИМЕНЕНИЕ НЕЧЕТКОЙ ЛОГИКИ ДЛЯ ОЦЕНКИ КРЕДИТНОГО РИСКА БАНКОВ
Аннотация
Ключевые слова
Полный текст:
PDF (English)Литература
Hannanova E.A. [Theoretical foundations for assessing creditworthiness]. Bulletin of Science and Education, 2016, no. 12 (24), pp. 46–48. (in Russ.)
Kostykova M.Yu. [Scoring model of small business lending and its improvement in the Russian Federation]. Finance and Credit, 2014, no. 15, pp. 57–64. (in Russ.)
Joshua Ignatius, Adel Hatami-Marbini, Amirah Rahman, Lalitha Dhamotharan, and Pegah Khoshnevis. A fuzzy decision support system for credit scoring. Neural Comput & Applic, 2018, no. 29, pp. 921–937. DOI: 10.1007/s00521-016-2592-1
Gorlushkina N.N., Shin E.V. [Reengineering of the lending business process and the use of the apparatus of fuzzy sets for the classification of borrowers in the problem of credit scoring]. Online magazine “Science of Science”. Economics and Management, 2015, vol. 7, no. 2, pp. 1–11. (in Russ.). Available at: https://naukovedenie.ru/PDF/82EVN215.pdf.
Hoffmann F., Basens B., Mues K., Van Gestel T., and Vantienen J. Derivation of descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. European Journal of Operational Research, 2007, pp. 540–555.
Ignatius J., Khatami-Marbini A., Rahman A. et al. Support system for fuzzy decisions for credit scoring. Neural calculations and applications, 2016, pp. 1–17. DOI: 10.1007 / s00521-016-2592-1
Xinhui C., Zhong Q. On Consumer Credit Scoring Based on Multi-criteria Fuzzy Logic. Proc. Int. Conf. Business Intelligence and Financial Engineering, 2009, BIFE'09. IEEE, 2009, pp. 765–768.
Lakhsasna A., Ainon R.N., Vakh T.Yu. Modeling decisions on credit risk assessment using an optimized fuzzy classifier. International Symposium on Information Technology 2008. IEEE, 2008, vol. 1, pp. 2–8.
Kudinov Yu.I., Ivanchenko K.S., Kudinov I.Yu. [Development of a fuzzy system for predic¬ting the quality of metal products]. Mechatronics, automation, control, 2008, no. 10, pp. 27–32. (in Russ.)
Labinsky A.Yu., Nefediev S.A., Bardulin E.N [Use of fuzzy logic and neural networks in automatic control systems]. Scientific-analytical journal Bulletin of the St. Petersburg University of the State Fire Service of the EMERCOM of Russia, 2019, no. 1, pp. 44–50. (in Russ.)
Pezeshki Z., Mazinani S.M. Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artif Intell, 2019, vol. 52, no. 1, pp. 495–525. DOI: 10.1007/s10462-018-9630-6
Xiaoying Su, Sihong Xu, Shaochuan Xu. Compound control system for coagulant dosing process based on a fuzzy cerebellar model articulation controller. Conference: 2017 36th Chinese Control Conference (CCC). IEEE, 2017, pp. 3931–3937. DOI: 10.23919/ChiCC.2017.8027972
Zade L. Ponyatie lingvisticheskoy peremennoy i ego primenenie dlya prinyatiya priblizhennykh resheniy [The concept of a linguistic variable and its application for making approximate decisions]. Moscow, Mir Publ., 1976. 167 p.
Yarushkina N.G., Afanasyeva T.V., Perfilieva I.G. Integratsiya nechetkikh modeley dlya analiza vremennykh ryadov [Integration of fuzzy models for time series analysis]. Ulyanovsk, 2010. 320 p.
Ozerova M.I., Zhigalov I.E. Application of neuro-fuzzy models in the information-analytical system of prediction of forest fires. CEUR Workshop Proceedings, 2019, pp. 389–396.
Novak V., Perfil'eva I., Mochkorzh I. Matematicheskie printsipy nechetkoy logiki. [Mathematical Principles of Fuzzy Logic]. Moscow, Fizmatlit Publ., 2006. 352 p.
Sadatrasoul S., Gholamian M., Shahanaghi K. Combination of Feature Selection and Optimized Fuzzy Apriori Rules: The Case of Credit Scoring. The International Arab Journal of Information Technology, 2015, vol. 12, iss. 2, pp. 138–145. Available at: http://www.ccis2k.org/iajit/PDF/ vol.12,no.2/5795.pdf.
Mammadli S. [Fuzzy Sistema ocenki kreditov na osnove nechetkoj logiki]. Procedia Computer Science, 2016, vol. 102, pp. 495–499. (in Russ.) Available at: https://doi.org/10.1016/j.procs.
Shi J., Xu B. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function. Journal of Risk and Financial Management, 2016, vol. 9, iss. 4, pp. 1–10. DOI: 10.3390/jrfm9040013
Borisov V.V., Kruglov V.V., Fedulov A.S. Nechetkie modeli i seti Fuzzy models and network. [Fuzzy models and networks]. Moscow, Hot line – Telecom, 2007, 284 p.
Ссылки
- На текущий момент ссылки отсутствуют.