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Power systems facing dynamic and nonlinear characteristics require effective optimization techniques to ensure stability, efficiency, and controllability. This paper proposes a method for optimizing power systems using a genetic algorithm that takes into account complex relationships in the power infrastructure. The genetic algorithm provides a flexible and versatile means for global optimization, providing robustness to local minima and adaptation to system dynamics. The method was applied to optimize the energy balance, minimize costs and take into account the nonlinear characteristics of the power system. Experimental results validated the effectiveness of the proposed method, demonstrating energy balance and high accuracy while accounting for complex system characteristics. However, further research is required to improve the method, including a more in-depth analysis of the influence of algorithm parameters and consideration of additional uncertainty factors. This research provides valuable insights into the field of power system optimization and provides a basis for the development of more effective control techniques in complex and dynamic energy environments. [ABSTRACT FROM AUTHOR]

This work is devoted to the development of a method for optimal load distribution between units of thermal stations using geometric programming. Load distribution problems for thermal power plants, including turbines, boilers, units and other components, are complex and relevant in the context of energy systems. The paper discusses optimization features for condensing turbine units, blocks and boilers. The use of geometric programming makes it possible to simplify the conditions for the most favorable distribution, providing effective methods for solving the problem. The developed method not only provides optimal load distribution, but also takes into account various technical characteristics and limitations of plant components. The results of the study represent a significant contribution to the field of optimization of energy systems, and the proposed method can be effectively used in practical problems of controlling thermal power plants to improve their efficiency and reliability. [ABSTRACT FROM AUTHOR]

The aim of the study is to develop models and algorithms for early diagnosis of diseases in cattle based on Sugeno's fuzzy inference, since the improvement and implementation of systems that allow early diagnosis of animal diseases based on modern information technologies is one of the most important tasks. To this end, the task of diagnosing animal diseases, their treatment and control by carrying out preventive measures with the further introduction of information and communication technologies in district and city departments and subordinate organizations has been solved. To solve these problems, theories of soft computing are used, the main component of which is fuzzy logic. The choice of a rational number of rules is made, while they can use the effective values of the membership function. This allows us to solve the task of diagnosing diseases in cattle, taking into account the setting of the parameters of the membership function to find an adequate solution. The scientific novelty is the construction of a knowledge base based on experimental data, the improvement of the Sugeno fuzzy logic model with symptoms of cattle diseases and the use of the Sugeno fuzzy algorithm to predict the causes, developmental features and clinical signs of diseases in cattle. Based on the developed program, the problems of classification, evaluation and comparison of the obtained results with the results of several algorithms are solved. The types and causes of cattle diseases are determined. The pr oblems of assessment, classification and prognosis of diseases according to seventeen signs have been solved. In accordance with accepted clinical practice, the types of diseases in cattle can be classified into the following diseases - microelementoses, osteodystrophy, secondary osteodystrophy and ketosis diseases. [ABSTRACT FROM AUTHOR]

The main goal of the study is to build a fuzzy Sugeno model for diagnosing diseases in cattle based on the development of a knowledge base. To achieve this goal, an analytical review of mathematical methods for assessing the state of animals, soft computing methods for solving classification problems was carried out. The scientific novelty of the research is the improvement of the algorithm for constructing a Sugeno fuzzy model based on the development of a knowledge base for diagnosing diseases in cattle and solving classification problems based on the theory of fuzzy sets and interval methods, as well as developing improved algorithms and building models for diagnosing diseases in cattle based on Sugeno's fuzzy inference. When constructing a Sugeno fuzzy logical model for diagnosing diseases in cattle, the optimal number of rules and, accordingly, the values of their membership functions were found. Membership function parameters were obtained from experts in the veterinary field. In the future, the parameters of the membership function were tuned using neural networks and evolutionary algorithms to obtain the minimum number of fuzzy rules. The tasks of assessing, classifying and diagnosing a disease in cattle are solved based on training in seventeen signs. Types of diseases are diagnosed, such as ketosis, osteodystrophy, secondary osteodystrophy and hypomicroelementosis, which must be recognized. Based on the proposed approach, a program for diagnosing diseases in cattle has been developed. On the basis of the developed program, practical problems are solved that provide data training with the creation of a model and visualization of the results obtained and the reliability and accuracy in diagnosing at all stages of using the program. The ease of use of the software package lies in the fact that it does not require the user to have in-depth knowledge of its structure and the operating system of the computer. [ABSTRACT FROM AUTHOR]

The state of fruit and vegetable exports is an important indicator of the quality of economic and social development of the economy. In this regard, in recent years, an active policy has been pursued to deepen economic reforms in the agricultural sector of the economy, create favorable conditions for increasing the production and processing of fruits and vegetables. As a result, the range of products - juices, nectars, jams, canned fruits and vegetables, marinades - is increasingly expanding in the markets and in the retail network. The main purpose of the work is to forecast options for the export of fruits and vegetables. To achieve this goal, the problems of developing a linear and non-linear regression model and a fuzzy Sugeno model for forecasting the export of fruits and vegetables were solved. The first method allows you to simultaneously tune the membership functions of the terms of the input variables and the conclusion of the rules by adjusting the coefficients of conclusions of fuzzy rules using the gradient optimization method. The collection of initial information and its preparation for calculations is the most time-consuming and responsible stage of statistical research. The success of the work largely depends on the completeness and reliability of the obtained economic information. The features of the application of the theory of fuzzy sets and neural networks are considered, which show their advantages in comparison with other existing methods when choosing a forecast model. [ABSTRACT FROM AUTHOR]