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12УДК: 681.518.3
BASICS OF OPERATION ALGORITHMS OF INTELLECTUAL SYSTEMS
Tashpolatova B.B., student, Daneev O.V., PhD. Econ. Sciences, associate Professor Financial university under the Government of the Russian Federation, Moscow, Russia
ОСНОВЫ АЛГОРИТМОВ ФУНКЦИОНИРОВАНИЯ ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМ
Ташполатова Б.Б., студент, Данеев О. В., к.э.н., доцент Финансовый университет при Правительстве Российской Федерации, Москва, Россия
Аннотация. Под «интеллектуальными системами управления» понимают допустимый по сложности комплекс элементов автоматизированного управления направленных на получение, обработку, применение некоторой второстепенной информации, понимание как «знание». Данные системы необходимы в первую очередь для работы в рамках неопределенности (отсутствие возможности существования наиболее точного математического воспроизведения) данных о характерных свойствах системно-сложных объектов, среды их деятельности.
Ключевые слова: система, интеллектуальная система, этапы построения интеллектуальных систем.
By "intellectual management systems" we mean restructuring provides a practical opportunity for
a complex of elements of automated control that are admissible in complexity aimed at obtaining, processing, applying some background information, understanding as "knowledge". These systems are necessary primarily for working within the uncertainty (the lack of the existence possibility of the most accurate mathematical reproduction) of data on the characteristic properties of system-complex objects, the environment of their activity. In the field of operation of existing systems with a high level of uncertainty of data for the formation of controls, the use of new information technologies aimed at flows of context-dependent information is not preventable, in other words, the formation of new moves in inventing intellectual management-the theory of intellectual control systems for systems of the highest degree. The theory of intellectual management systems is based on a systematic approach in the sense that it concentrates on systemic rather than descriptive complexity, assigns the system to the outside world and recognizes the functioning of an internal complex installation, at least at the stage of maintaining the sustainability of one's own existence. Intellectual management appears where information is interpreted as a quantitatively unregulated data set. In order to recognize management as a conscious flow of information, it is important to use the database provided that the context and the message relationships are unchanged and can be prescribed by the final set of records, as well as the value of the information is difficult enough, the context is variable, the management goal is corrected during operation, which requires the restructuring of the internal data during the adjustment of the information movement process. The necessary
active perception of information and is a characteristic segment of the emergence of intellectual management.
Principles of the construction and organization of intellectual management systems
The study of intellectual systems makes it possible to identify complex tendencies, which, not being full, show important moments in their activities, organizations.
1. Principle of system. Intellectual systems exist only in a complex form, the options of all their elements need to be coordinated with the task of the system. Mutual balance, the consistency of the segments of the system generates the complexity and highly functional completeness of the most holistic intellectual systems.
2. The principle of hierarchy. A fairly complex hierarchical combination is an attribute for a simultaneous flow of stages. The complex dynamics of stages is also deliberately described by a complex structure. The nature of the structure of the system, the degree of complexity predetermines and the type of its intelligence.
3. The principle of multi-channel. Obtaining mutually correlated solutions with different levels of tasks is based on information that travels through a variety of channels and functions on many physical trends, which makes it possible to have all possible individual characteristics of the properties of the components of the medium. The generalization of the information material makes it possible to have the most complete idea of the projects being implemented.
Any person is able to perform identification tasks of different degrees in a split second, the human visual system definitely works as a synchronous
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device. Parallel ranking of visual information, delivered to the human brain from the rest of the sense organs, allows for a commensurate decoding of the object.
4. The principle of adaptability. It presupposes the existence of probabilistic activities in intellectual systems for the improvement of their functioning: within the framework of a preliminary, current uncertainty based on learning from experience.
Adaptation is due to self-organization. Adaptive capabilities are identified by the memory of the system, the necessary time required for its processing.
5. Principle of reciprocity of functional, structural properties. The task of the system mainly affects the integrity of the system.
6. The principle of equivalence. The principle determines the existence of a variety of mutually connected processes of reactions to specific external factors to a single functional outcome.
7. The principle of dynamic self-programming. One of the important possibilities of nervous control is based on the ability to reproduce complex, first-class programs of activity based on a variety of analysis of specific situations, which are continuously tuned taking into account past events, current significance and predicting the future.
Types of intellectual measuring systems
1. Expert systems. The expert system is a computer program that has the potential to some extent of a specialist expert in resolving the emerging problem issue. The experimenters of intelligence began the creation of ES in the 1970s. Expert systems can be studied in complex with knowledge bases as models of experts' behavior in a specific field of knowledge, applying methods of logical inference, decision making, and knowledge bases as a set of trends, norms of logical inference in the chosen subject field of activity. Similar activities, tasks are carried out by a wizard. Wizards are used in system programs. The key difference between masters from expert systems is the lack of a knowledge base; functions are programmed. This is a real complex of structures for filling by the user. Other similar programs, such as encyclopedic, are search engines. They are considered the most advantageous segments of the article base at the request of the user.
2. Hybrid intellectual systems. The system for calculating the task uses more than one method of simulating the intellectual functionality of a person. Hybrid intelligent systems are characterized by a multitude of analytical models, expert systems, simulation statistical models.
3. Intellectual information systems. Intellectual information system - modification of the intellectual system. Often AIS is defined as a knowledge-based system. IIS is a combination of linguistic, software, logical-mathematical methods for realizing the goal: realization of support of human functioning, recognition of information in the mode of modern dialogue in natural language.
The concept of intellectual measuring devices
Intellectual measuring instruments are usually a variety of devices - intelligent sensors, automated installations, which are a complex of mechanisms for the transfer and processing of data, taking into account the use of intelligent methods on the alternative to the knowledge base. In the practice of measuring improvements in multidimensional arrays of information, a number of basic tasks become: sorting, aligning signals on an individual basis, ranking multiple signals into groups, addressing one of the transmission channels, which is influenced by a signal with specific established information characteristics, controlling the existence of the established rank structure of the complex Signals. Very often, the meaning of "intellectual" is used in the narrow sense in relation to the mechanisms that, due to the application of information transformation in them, acquire completely different functionality. The intelligent sensor produces fairly accurate data by using numerical calculations, while compensating for the nonlinearities of the sensitive segment. This sensor has the ability with a greater variation of the kinds of sensitive segments, to rank two or more dimensions into one new one. The intelligent sensor makes it possible to configure tuning and other measuring ranges, semi-automatic calibration, to perform internal self-organization, which makes maintenance easier enough. Along with the modification of the work, the secondary significant capabilities of intellectual systems reduce the dimensionality of signal changes by the control system and lead to the fact that a number of variant mechanisms are replaced by a device of one model, which gives a big gain in the cost of service in production. The simplest measurement scheme usually includes: a sensor that is connected to a system for ranking its signals-a specific processor used to process data signals at the hardware level, and a computer, a microcontroller that is provided with a data conversion program from this sensor.
Principles of organization construction, the
structure of intellectual measuring tools
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The various segments of the intellectualization of measurement methods, such as the non-recognition of rigid algorithms for their activities, the use of existing subsequent and current information, the option of taking actions depending on the acquired measurement results with a priori changes in the measurement mechanism, self-organization, are carried out in various currently developing measurement methods, e.g. automated systems of metrological tests and so on.
The study of the characteristics of the construction of the basic structures of intellectual measuring systems is carried out on the basis of visualization about the type of their activities that are consistent with the established principles of the activity of intellectual computers. The cycle of intellectual system consists of four processes:
At the first stage, intellectual system takes the initial information about the properties of the measurement task, the nature of the measurements, the established requirements, the constraints imposed. The format of the initial information makes it possible to identify the measurement process displayed by the characteristic features of the measurement object and the imposition of a number of measurement steps, the implementation of which is possible on the basis of incoming hardware devices.
Executive part
Fig.1 Structure of an intellectual measuring instrument
At the second stage, the process is recognized, a certain number of measurement algorithms is set.
The third stage is based on the choice of the best measurement method from the number represented. Since the norm of the choice of this algorithm, with the unavoidability associated with the requirement of accuracy characteristics of the results of measurements in the application of the compared stages, the measurement knowledge should include the information necessary for the implementation of the corresponding calculation procedures, simulation modeling. After the selection of the measurement algorithm, the final, fourth stage of necessary measurements is carried out. To implement the described cycle of work, Romanov proposed to include the following main parts in intellectual in 1994 (see Figure 1.1):
• executive part (EP) - realizes the selected measurement procedure;
• the base of measuring knowledge - consists of knowledge bases and data, including all necessary information and designed in the form of corresponding arrays, sets of dependencies, mappings and relations, as well as a database containing basic information about intellectual systems;
Intellectual interface (Inl) - includes the necessary software for obtaining the initial information, using knowledge, controlling the HI and issuing the results of measurements. Intellectual measuring systems are systems that can be universally programmed to perform characteristic functions using a programmable terminal in order to enter the configuration coefficients. In conclusion, it is important to note that intelligent measurement systems, devices are increasingly absorbed in human activities, playing a very important role in it. Over time, intellectual systems will function in all areas of human life.
Bibliography
1. Intellectual means of measurement: a textbook for students of higher educational institutions / GG Rannev. - M.: Publishing Center "Academy", 2010. - 272 p.
2. Valery Shevchuk, Vyacheslav Kuzevanov, Egor Kaplya "Modeling of control processes in intelligent measuring systems", 2015 - 512 p.
3. Zvyagin LS System analysis and mathematics: the synthesis of sciences in modern higher education / / Education and upbringing. 2017. No. 1 (11). Pp. 58-63.
4. Zvyagin LS Information-cybernetic studies and design of complex systems / / Engineering. Technologies. Engineering. - 2017. - №2. - P. 2127.
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5. Zvyagin LS, Daneev OV "Methods of carrying out case studies. Textbook-M.: Financial financial calculations". Collection of tasks for university, 2016.
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УДК 338.1
АНАЛИЗ РАСПРЕДЕЛЕНИЯ СРЕДНЕДУШЕВЫХ ДОХОДОВ НАСЕЛЕНИЯ С
2008 ПО 2017 В РОССИИ
Тараканова О.И., студентка Научный руководитель: Данеев О. В., к.э.н., доцент ФГОБУ ВО «Финансовый университет при Правительстве Российской Федерации», Россия, г.
Москва.
Аннотация: во все времена перед человечеством стояла проблема справедливого распределения доходов. Целью данной работы является поиск теоретического закона распределения денежных доходов населения в России в периоде с 2018 по 2017 гг.
Ключевые слова: нормальное распределение, денежные доходы населения, прогноз доходов, уровень жизни, распределение Стьюдента, материальные блага,
ANALYSIS OF THE DISTRIBUTION OF PER CAPITA INCOME FROM 2008 TO 2017 IN
RUSSIA
O. Tarakanova, student Scientific supervisor: O.V. Daneev, Ph. D., associate Professor, Financial University under the Government of the Russian Federation, Moscow, Russia.
Abstract: at all times mankind faced the problem of equitable distribution of income. The purpose of this paper is to search for a theoretical law of distribution of monetary income of the population in Russia in the period from 2018 to 2017.
Key words: normal distribution, monetary incomes of population, income forecast, standard of living, student distribution, material benefits.
Развитие России пошло по осложненному пути изменения на фундаментальном уровне структуры рыночных отношений и экономики, где главной проблемой стало усиливающее расслоение общества. Как результат снижение покупательской способности и доходов населения в 90-х. Что не могло не сказаться на уровне и продолжительности жизни. После реформ 2000-х годов в России наблюдался экономический подъем. Рос уровень доходов, и, как результат, росла покупательская способность населения.
Объект исследования - генеральная совокупность среднедушевых доходов населения по Российской Федерации с 2008-2017 гг.
Целью данной работы является поиск теоретического закона распределения случайной
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величины, опираясь на эмпирическое распределение этой величины, полученное в результате выборочного наблюдения.
Для решения этой задачи выдвигается 2 гипотезы:
Гипотеза 0 (в решении задачи Ж) -Распределение среднего денежного дохода населения по Российской федерации в % по отношению к предыдущему периоду нормально т.е. % который есть - распределен по закону X~N(m,D)
Гипотеза 1 (Ш) - Распределение среднего денежного дохода населения по Российской федерации в % по отношению к предыдущему периоду не нормальное
Для анализа мы используем выборку среднедушевых денежных доходов населения по
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