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Международная научно-практическая конференции «Прикладные аспекты мягкого моделирования в управлении в условиях цифровой трансформации»

20 апреля 2021 года в рамках X Социально-экономического Форума «Интеллектуальные ресурсы - региональному развитию» ЮЖНЫЙ УНИВЕРСИТЕТ (ИУБиП)  проводит международную научно-практическую конференцию «Прикладные аспекты мягкого моделирования в управлении в условиях цифровой трансформации». Актуальность тематики конференции обусловлена возрастающим интересом к вопросам нечетной логики в мировом научном сообществе, а также сферой научной проблематики ведущих вузов Ростовской области, отраженной в публикациях.


Информационное письмо Прикладные аспекты мягкого моделирования в управлении в условиях цифровой трансформации

Требования к оформлению тезисов

           Наработки ростовских ученых прошлых лет по тематике мягких вычислений нашли отражение в первом томе монографии, изданной в 2019 году ЮЖНЫМ УНИВЕРСИТЕТОМ (ИУБиП).

Монография «Soft models of management in terms of digital transformation»


SOUTHERN UNIVERSITY (IMBL) build a serious strategy for the transformation of the University to train specialists for the digital age. The methodological platform of this transformation of the University is soft modeling for the management of the University scientific and educational, socio-economic and humanitarian-legal complex.

The conditions for the functioning of socio-economic, humanitarian, legal, technical, technological and other facilities and systems are becoming more complex from year to year. Accordingly, the objects and systems themselves are complicated and improved. Their intelligence is increasing: the coefficient of machine intelligence (CMI) is rapidly approaching the natural IQ. There are a number of problems arising from this trend, including the need for adequate communication between the carriers of natural and artificial intelligence, rethinking the place of man in the new digital reality, the restructuring of many technical, technological, socio-economic, humanitarian and legal processes.

Despite the improved information support for decision-making, the expansion of communication between the objects included in the system and the systems themselves, the level of uncertainty and fuzziness is not reduced, but rather increases. The increase in the amount of data, facts and information that accumulate from year to year in the databases of information systems at all levels and in the world information network, leads to a significant increase in the entropy of information. Therefore, the statement that the decision-maker will never have all the information necessary to fully justify his choice is true today [Zadeh L. A. the Role of soft computing and fuzzy logic in the understanding, design and development of information intelligent systems. - News of Artificial Intelligence, №2-3, 2001, p. 7 - 11].

Fuzzy computing (Fuzzy Computing) is one of the methods of soft computing (Soft Computing), which in addition to working with fuzzy, include areas such as: neural calculations, genetic calculations, self-learning systems, Bayesian inference, etc. the Main difference between soft computing from traditional, hard is the adaptation to the "comprehensive inaccuracy of the real world." The guiding principle of soft computing is: "tolerance for inaccuracy, uncertainty and partial truth to achieve ease of manipulation, low cost of solution, and better agreement with reality" [ibid.]. The initial model for soft computing is human thinking.

Following the conference, the second volume of a unique monograph in English will be released.


The book explores the problems and methods of soft modeling and control of poorly defined systems under fuzzy under determination. The necessity of intellectualization and digitalization of a wide range of soft modeling methods based on fuzzy and neuro-fuzzy, granular approach is substantiated. The monograph consists of five parts devoted to the development and research of soft models based on fuzzy, neuro-fuzzy, chaotic and probabilistic approaches. Schemes of fuzzy controllers are given and their structures are described. The results of full-scale and computer modeling are presented. Fuzzy and soft models of complex systems are actively introduced in industry, communications, energy, transport systems, research, social and economic research and other areas. Examples of the use of fuzzy models and regulators in various industries, agriculture and transport for forecasting and management.