name
Lutsenko Yevgeniy Veniaminovich
Scholastic degree
•
Academic rank
professor
Honorary rank
—
Organization, job position
• Kuban State Agrarian University
кафедра компьютерных технологий и систем
профессор
Research interests
Системно-когнитивный анализ, системы искусственного интеллекта, высшие формы сознания, перспективы человека, технологии и общества
Web site url
Current rating (overall rating of articles)
0
TOP5 co-authors
Articles count: 276
Сформировать список работ, опубликованных в Научном журнале КубГАУ
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Description
In this article, on a small and evident numerical example, methodological aspects of a process engineering of detection of knowledge from the trial-and-error data explicitly are considered, representation of knowledge and its usage for problem solving of forecasting, decision making and data domain examination in system-cognitive analysis (SC-analysis) and its programmatic toolkit - intellectual "Eidos" system are shown
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Description
The processing complex of a region is considered as a multi-level hierarchical active reflective system, which is the object of intellectual control. The economic stability of the regional processing complex is considered as one of the most important because of its characteristics, as they considerably affect the quantitative and the qualitative results of the work. The system-cognitive approach to the construction and verification of the system of intellectual models of processing of the regional complex is implemented. We have selected the most adequate model of the processing complex of the region, in which we explore the issues of management of its economic stability
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Description
Methodology of using systemic cognitive analysis for building multi-level semantic information model of agro-industrial holding management is formulated in the article in general. Based on this, solutions of forecasting problems and support of decision-making process of management and scientific researches are listed
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METHODS OF REDUCING SPACE DIMENSION OF STATISTICAL DATA
01.00.00 Physical-mathematical sciences
Description
One of the "points of growth" of applied statistics is methods of reducing the dimension of statistical data. They are increasingly used in the analysis of data in specific applied research, such as sociology. We investigate the most promising methods to reduce the dimensionality. The principal components are one of the most commonly used methods to reduce the dimensionality. For visual analysis of data are often used the projections of original vectors on the plane of the first two principal components. Usually the data structure is clearly visible, highlighted compact clusters of objects and separately allocated vectors. The principal components are one method of factor analysis. The new idea of factor analysis in comparison with the method of principal components is that, based on loads, the factors breaks up into groups. In one group of factors, new factor is combined with a similar impact on the elements of the new basis. Then each group is recommended to leave one representative. Sometimes, instead of the choice of representative by calculation, a new factor that is central to the group in question. Reduced dimension occurs during the transition to the system factors, which are representatives of groups. Other factors are discarded. On the use of distance (proximity measures, indicators of differences) between features and extensive class are based methods of multidimensional scaling. The basic idea of this class of methods is to present each object as point of the geometric space (usually of dimension 1, 2, or 3) whose coordinates are the values of the hidden (latent) factors which combine to adequately describe the object. As an example of the application of probabilistic and statistical modeling and the results of statistics of non-numeric data, we justify the consistency of estimators of the dimension of the data in multidimensional scaling, which are proposed previously by Kruskal from heuristic considerations. We have considered a number of consistent estimations of dimension of models (in regression analysis and in theory of classification). We also give some information about the algorithms for reduce the dimensionality in the automated system-cognitive analysis
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01.00.00 Physical-mathematical sciences
Description
The article considers measuring scales as a tool for creating formal models of real objects and a tool for increasing the degree of formalization of these models to a level sufficient to implement them on computers. It also describes the different types of measuring scales, allowing to create models of varying degrees of formalization; lists the types of transformation valid during the processing of empirical data obtained with scales of different types; develops the task of metriza-tion of the scales, i.e. conversion to the most formalized mind; it proposes 7 ways of metrization of all the types of scales, providing a joint comparable quantitative processing of heterogeneous factors measured in different units of measure due to the conversion of all scales to one universal unit of measurement in which the measurement number of information is selected. All of these methods of metrization have been implemented in the system-cognitive analysis and in the Eidos intellectual system
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SIMULATION OF EARTH'S POLES DYNAMICS USING ASK-ANALYSIS
Description
Based on local semantic information models, we have examined the dependence of the dynamics of the displacement of the pole positions of celestial objects. We have also developed and differentiated an analysis of ASK-pole modeling of dynamics within sixty-year cycles of reference points and substantiated reasons for the population inversion and singular states in the dynamics of the pole
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SIMULATING AND PREDICTING GLOBAL CLIMATIC ANOMALIES SUCH AS EL NINO AND LA NINA
01.00.00 Physical-mathematical sciences
Description
The paper discusses the modeling and prediction of the climate of our planet with the use of artificial intelligence AIDOS-X. We have developed a number of semantic information models, demonstrating the presence of the elements of similarity between the motion of the lunar orbit and the displacement of the instantaneous pole of the Earth. It was found that the movement of the poles of the Earth leading to the variations in the magnetic field, seismic events, as well as violations of the global atmospheric circulation and water, and particular to the emergence of episodes such as El Niño and La Niña. Through semantic information models studied some equatorial regions of the Pacific Ocean, as well as spatial patterns of temperate latitudes, revealed their relative importance for the prediction of global climatic disturbances in the tropical and temperate latitudes. The reasons of occurrence of El Niño Modoki and their relationship with the movement of elements of the lunar orbit in the long-term cycles are established. Earlier, we had made a forecast of the occurrence of El Niño episode in 2015. Based on the analysis of semantic models concluded that the expected El Niño classical type. On the basis of the prediction block AIDOS-X calculated monthly evolution scenario of global climate anomalies. In this paper, the analysis of the actual implementation forecast of El Niño since its publication in January 2015 - before June 2015. It is shown that the predicted scenario of climatic anomalies actually realized. Calculations of future climate scenarios with system «Aidos-X» recognition module indicate that further possible abnormal excess temperature indicators of surface ocean waters in regions Nino 1,2 and Nino3,4 for 2015 may be comparable with similar abnormalities in the catastrophic El Niño of 1997-1998.
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01.00.00 Physical-mathematical sciences
Description
In the article, we have considered the application of a system-cognitive analysis and the Eidos-X++ intellec-tual system to create complex multifactor models of nonlinear control objects on the basis of noisy frag-mented empirical data of large dimension and for the use of these models to solve problems of forecasting, executive decision making and research of the model objects. We have formulated the systematic generalization of the principle of Ashby (for nonlinear systems). The numerical example of a study of an abstract nonlinear system (Lissajous figures), in which the combined effect of multiple factors is the sum of the influences of each of these factors separately, that says about non-compliance of these factors, the principle of superposition and nonlinear effects in the system under consideration. It is shown, that the proposed device and software tools allow us to model such systems. We note, that the proposed device and instrumentation allow to interpret some classification scale, as projected geographical coordinates of the event, and others, like the foreseeable events and their severity, which allows you to get cartographic visualization of recognition of the place and time of events
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01.00.00 Physical-mathematical sciences
Description
The method of ordinary least squares (OLS) is widely known and deservedly popular. However, some attempts to improve this method. The result of one of such attempts is the weighted least squares (WMNC), the essence of which is to give the observation a weight which is inversely proportional to the errors of their approximation. Thereby, in fact, monitoring is ignored the more the difficult to approximate it. The result of this approach, formally, is the approximation error decreasing, but in fact, this occurs by partial refusal to consider the "problem" of observations, making a big mistake. If the idea underlying WMNC to bring to the extreme (and absurd), then in the limit, this approach will lead to the fact that from the entire set of observations there will be only those that lie almost exactly on the trend obtained by the method of least squares, and the rest will simply be ignored. However, according to the author, it's not a problem, and the failure of its decision, though it might look like a solution. In the work we have proposed a solution, based on the theory of information: to consider the weight of observations, the number of the argument of the value function. This approach was validated in the framework of a new innovative method of artificial intelligence: methods for automated system-cognitive analysis (ASA-analysis) and implemented 30 years ago in its software toolkit, which is "Eidos" intelligent system in the form of so-called "cognitive functions". This article presents an algorithm and software implementation of this approach, illustrated in detailed numerical example. In the future it is planned to give a detailed mathematical basis of the method of weighted least squares, which is modified by the application of information theory to calculate the weights of the observations, and investigate its properties
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01.00.00 Physical-mathematical sciences
Description
This article briefly discusses the mathematical nature of the author's proposed modification of the weighted least squares, in which the amount of the data is used as the weights of observations. There are two variants of this modification. In the first one, the weighting of the observations was made by replacing one observation with a certain amount of the information in it by the corresponding number of observations for unit weight, and then we applied the standard method of least squares. In the second method, the weighting of the observations was performed for each value of the argument by replacing all observations with a certain amount of information in one observation of unit weight which had been obtained as a weighted average of them, and then we applied the standard method of least squares. We have described in detail the technique of numerical calculations of the amount of information in the observations, based on the theory of automated system-cognitive analysis (ASC-analysis) and implemented it with a help of software tools - intelligent system called "Eidos". The article provides an illustration of the proposed approach on a simple numerical example. In the future, we are planning to give more detailed mathematical basis of the method of weighted least squares, which is modified by using the amount of information as weights, but also to explore its properties
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