Evolutionary calculations, bioinspiration methods, hybrid intelligent systems, computational intelligence, fuzzy genetic algorithms, fuzzy methods and control systems, graph theory, optimization methods
Head of the grant of the Russian Foundation for Basic Research N 11-01-00122 Development of the theory and principles for constructing intelligent hybrid fuzzy genetic, evolutionary and adaptive decision-making methods in the design and optimization. Participated in the following scientific research: 1. Grant of the Russian Foundation for Basic Research 09-01-00492 Development of a general theory and cognitive principles of evolutionary computing. 2. Grant of the Russian Foundation for Basic Research 09-01-00509 Development of the theory and principles of evolutionary optimization and decision-making based on bionic modeling. 3. Grant of the Russian Foundation for Basic Research 10-01-90017 Development of the fundamental foundations for solving intellectual problems on high-performance computing systems. 4. Grant of the Russian Foundation for Basic Research 11-07-00094 The theory of evolutionary and granular computations for the construction of information systems based on knowledge. 5. Grant of the Russian Foundation for Basic Research 12-07-00062 Theory and basic principles of the development of information problem-oriented systems for representing heterogeneous knowledge based on the methods of evolutionary modeling. 6. Grant of the Russian Foundation for Basic Research 13-01-00698 Development of hybrid models for scheduling and operational planning based on the theory of adaptation and multi-agent technology. 7. Grant of the Russian Foundation for Basic Research 13-07-12091 Development of new information and computational technologies for optimizing the structure of regional transport transportation on the basis of hybrid intellectual models and methods of swarm intelligence. 8. Grant of the Russian Foundation for Basic Research 15-07-05523 Development of the theoretical foundations of bioinspiral search for creating problem-oriented information systems related to the solution of logistics tasks. 9. Grant of the Russian Foundation for Basic Research 16-01-00715 Development of hybrid intelligent subsystems to support scheduling and operational planning based on fuzzy evolutionary methods. 10. Research work on the assignment of the Ministry of Education and Science of the Russian Federation 1.04.01 Development of a theory and principles for the construction of intelligent decision-making systems in the design based on quantum computing and bionic search methods. 11. Target departmental analytical program for developing the potential of the scientific school (RNP 2.1.2.1652) Development of the theory and cognitive principles of decision-making on the basis of distributed algorithms inspired by natural systems. 12 Research work on the assignment of the Ministry of Education and Science of the Russian Federation N 37.04.54 (12.8.08) Development of the theory and principles of data mining for the construction of decision support systems. 13. Research work on the assignment of the Ministry of Education and Science of the Russian Federation 37.04.53 Development of the theory and principles of evolutionary optimization and decision-making on the basis of methods and models of adaptive behavior of biological systems. 14. Research work on the assignment of the Ministry of Education and Science of the Russian Federation 8.823.2014/K (project part) Development of the theory and basic principles of evolutionary computation to support the adoption of optimal solutions in the design of multi-purpose intelligent systems. 15. Grant of the Russian Science Foundation 14 - 11 - 00242 Development of the theory of bioinspired search and processing of problem-oriented knowledge in the design of intelligent information systems.
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Optimization methods
The purposes of mastering the discipline "Optimization methods", correlated with the general objectives of the educational program in the relevant area of training: satisfaction of the educational needs of the individual in mastering the fundamental facts and principles of constructing software software; Satisfaction of the needs of customers in qualified specialists with higher education and scientific and pedagogical staff who have the skills to build mathematical models, are able to apply optimization methods to solve problems, effectively organize and use in modern digital computer systems optimal modes and methods for solving real project tasks.
Content of the discipline
1. Basic concepts of optimization theory (optimization objects, classification of problems and optimization methods, classical optimization methods).
2. Basic models and methods for solving optimization problems (general mathematical programming problem, convex programming, unconditional and conditional optimization methods for one and many variables functions, linear programming, nonlinear programming, integer programming, nonparametric optimization, stochastic optimization, dynamic programming, optimization on Graphs and networks).
As a result of mastering the discipline "Methods of optimization", the trainee must:
Know: the basic definitions of optimization theory; Classification of optimization methods; Existing methods for solving optimization problems.
To be able: to apply methods and algorithms of optimization; Solve single-criterion and multi-criteria optimization problems; Use the methods, skills and modern technical literature necessary for engineering practice; To develop and use in practice mathematical models of optimization problems.
To master: methods of scientific search; Information about the main methods and algorithms used in solving optimization problems; Methods of constructing mathematical models of optimization problems; Skills in the practical solution of optimization problems.
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Models and methods of analysis of design solutions
The objectives of mastering the discipline "Models and Methods for Analyzing Project Solutions" correspond to the objectives of the state educational standard implemented at the Southern Federal University and read for the direction of preparation 090301 "Computer Science and Computer Engineering" of the "Computer-Aided Design" training profile:
Satisfaction of the educational needs of the individual in mastering the fundamental foundations and principles of constructing mathematical models of elements and systems of modern digital and analogue electronic equipment, methods of analyzing the adequacy of the models used and the quality of design decisions, modern computer technology for solving complex problems in the field of computer-aided design;
Satisfaction of the needs of customers in qualified specialists with higher education and scientific and pedagogical staff who can develop, design and use computer technology and effectively apply modern and promising methods and models for analyzing project solutions;
The improvement of the professional component of education in the field of "Automation of design" in the field of "Computer science and computer technology" by participating in the implementation of fundamental and applied scientific research, grants, federal and departmental target programs and state contracts.
Content of the discipline
Mathematical models of design objects. Classification of mathematical models of CAD. Characteristics and methods of forming mathematical models of design objects used at micro, macro and meta levels. Mathematical models of analog and digital electronic equipment.
Methods of analysis of design objects. Classification of applied methods of analysis. Directions of increasing the effectiveness of methods of analysis. Numerical methods for solving systems of linear algebraic equations and ordinary differential equations. Methods of analysis of increased efficiency and multivariate analysis. Methods of analysis of logical and functional circuits of electronic computing equipment.
As a result of mastering the discipline "Models and Methods for Analyzing Project Solutions," the trainee must
Know:
The main criteria for assessing the quality of design solutions;
Methods for analyzing the quality of design decisions;
Existing methods of constructing mathematical models of design objects.
Be able to:
Independently build mathematical models of projected elements and systems;
Use the methods, skills and technical literature necessary in practice;
To carry out the analysis and an estimation of accepted design decisions.
Own:
Basic methods and algorithms for optimization and synthesis of design solutions;
Methods of analyzing the quality of the resulting design solutions;
Methods of constructing and solving mathematical models of varying degrees of detail for design objects.
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Theoretical foundations of CAD
The purposes of mastering the discipline "Theoretical Foundations of CAD" correspond to the objectives of the state educational standard implemented at the Southern Federal University and read for the direction of preparation 090301 "Computer Science and Computer Engineering" of the profile of the "Computer Aided Design" training:
Satisfaction of the educational needs of the individual in the competent use of advanced computer equipment, convenient and effective management of it, professional mastering of the fundamental principles and foundations of the construction, operation and use of modern digital computers, mechanisms and means to support the processes of solving problems on the computer or in the environment of the CAD ). This goal is consistent with the goals of CAD: to help people find the best solutions to the problems of automated design of various objects and to participate in their improvement and development by providing an opportunity to obtain high-quality higher education in the field of computer science and computer technology;
Satisfaction of the needs of customers in qualified specialists with higher education and scientific and pedagogical staff who can develop, design and competently operate modern and prospective digital computer systems (DCS), effectively organize and use in DCS optimal modes and methods for solving real project tasks;
The improvement of the professional component of education in the direction of computer science and computer technology in the profile of the computer-aided design system by carrying out and using the results of fundamental and applied scientific research obtained in the process of implementing grants from the Russian Foundation for Basic Research, federal and departmental target programs and state contracts.
Content of the discipline
The main stages of design. Scheme of the design process and analysis of the possibility of using a computer. General provisions and tasks of creating CAD. Requirements for CAD. Architecture of CAD. Classification and varieties of CAD. Methodology for building CAD.
Types of CAD support. Types of CAD systems. Systems of computer-aided design in radio electronics. Examples of ECAD programs. Design tools of CADENCE, SYNOPSYS, MENTOR GRAPHICS.
As a result of mastering the discipline "Theoretical Foundations of CAD", the student should
Know:
Basic stages and stages of the design process;
The main types of computer-aided design systems;
Existing methods and approaches to the organization of the process of computer-aided design;
The main types of CAD support;
Modern requirements for the construction and organization of information management systems, design and decision-making;
Main types of CAD and examples of their application in radio electronics and engineering.
Be able to:
To evaluate the advantages and disadvantages of CAD systems, their applicability for solving each specific problem;
To use independently the received knowledge for the decision of the actual problems connected with a choice, the organization and construction of systems of the automated designing;
To work with the basic packages of software products for the computer-aided design of the company.
Own:
Methods of scientific search;
Basic methods and algorithms for constructing components of software complexes and subsystems of computer-aided design;
The basics of working with existing computer-aided design software packages.
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Perspective methods and algorithms for solving technical problems
The purposes of mastering the discipline "Perspective methods and algorithms for solving technical problems" are:
Satisfaction of the educational needs of the individual in the competent use of advanced computer equipment, convenient and effective management of it, professional mastering of the fundamental principles and foundations of the construction, functioning of information systems. This goal is consistent with the objectives of the discipline.
Satisfaction of the needs of customers in qualified specialists with the highest category and scientific and pedagogical staff who can develop, design and competently operate modern and promising digital computer systems (DCS), effectively organize and use in DCS optimal modes and methods for solving real project tasks;
The improvement of the professional component of education in the field of computer science and computer technology by carrying out and using the results of fundamental and applied research received in the process of implementing grants of the Russian Foundation for Basic Research, federal and departmental target programs and state contracts, grants of the Russian Science Foundation.
The purposes of mastering the discipline correspond to the objectives of the state educational standard in the field of preparation 09.06.01 "Informatics and computer technology".
The process of studying the discipline is aimed at forming the elements of the following competencies in accordance with the educational standard in this area of training (specialty):
The ability to improve and develop their intellectual and general cultural potential;
The ability to independently learn new methods of research, change the scientific and scientific and production profile of their professional activities;
The ability to critically analyze and evaluate current scientific achievements, generate new ideas in solving research and practical problems, including in interdisciplinary areas;
The ability to design and carry out complex studies, including interdisciplinary, on the basis of an integral systematic scientific worldview using knowledge in the field of history and the philosophy of science;
The ability to plan and solve problems of their own professional and personal development;
Possession of the methodology of theoretical and experimental research in the field of professional activity;
Possession of a culture of scientific research, including using modern information and communication technologies.
As a result of mastering the discipline "Perspective Methods and Algorithms for Solving Technical Problems," the trainee must
Know:
The fundamentals of the methodology of science and the methods of scientific research;
Basic measures of information and ways of its adequate presentation;
The theory of modern bioinspiral and quantum algorithms;
Architecture of multiprocessor computers, transputers;
Methods of solving the problems of modern discrete mathematics, ways of building promising information technologies necessary for professional development of the graduate student;
Elements of modern set theory;
Technologies for creating expert systems;
Be able to:
Carry out theoretical and experimental research in the field of constructing logical schemes of modern computing devices at a high professional level;
Independently apply the obtained skills of classification and coding of information to solve professional problems in related fields;
To use elements of the theory of infinite sets and multisets in scientific and project activity;
Design reconfigurable systems;
To use the theory of modern algorithms for the professional purposes of the graduate student;
Perform operations with multisets;
To develop integrated automated information systems;
Own:
Methods of scientific search;
The ability to learn new methods of research on the basis of studying the methods of modern discrete mathematics, fuzzy logic, the design of information systems;
The theory of modern discrete mathematics by modern set theory;
Methods of system engineering, circuit and design design of modern computers;
Ways to assess the computational complexity of algorithms;
Skills in the practical use of multisets;
Skills of realizing models for real objects.
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Intellectual Data Analysis and Bioinspirated Methods and Algorithms
The objectives of the discipline "Intellectual Data Analysis and Bioinspired Methods and Algorithms" are:
Satisfaction of the individual's needs for intellectual development by providing her with an opportunity to obtain a high-quality higher education in the field of computer science, computer science and artificial intelligence.
Satisfaction of the needs of customers in qualified specialists with higher education and scientific and pedagogical staff of the highest qualification, who possess methods inspired by natural systems, artificial intelligence currently used in the construction of intellectual CAD subsystems in the field of computer science, computer technology. This goal is consistent with the objectives of the magistracy: to help people find the best options for solving problems of optimization and automated design of various objects and to participate in their improvement and development by providing an opportunity to obtain quality higher education
As a result of mastering the discipline "Intellectual Data Analysis and Bioinspired Methods and Algorithms" the student should:
Know:
Basic information about the means and methods of using computer technology in solving problems of data mining;
Natural science concepts, definitions and terms borrowed from biology, genetics, the theory of evolution used in the theory of evolutionary computations, as well as in the process of creation and practical use of methods and algorithms inspired by natural systems;
Be able to:
To specify and plan the content of automated information systems and management systems for intellectual data analysis;
Depending on the problem to be solved, to apply certain methods and models and build bioinspiral algorithms;
Own:
The skills of developing intelligent information systems, working in tool environments for creating automated data mining systems;
The skills of working on the compilation of mathematical models, the formulation of optimization problems, the selection of operators and the programming of bioengineered algorithms.
