Parameters Identification Method of Interval Discrete Dynamic Models of Air Pollution Based on Artificial Bee Colony Algorithm

Dyvak, Mykola

doi:10.1109/acit49673.2020.9208972

Cited by 24 publications

(32 citation statements)

References 17 publications

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“…However, to date, the most effective methods for solving this optimization problem are methods based on behavioral models of artificial bee colonies (ABC) [49]. e substantiation of this fact is given in [40,44].…”

Section: Methods Of Mathematical Modeling Based Onmentioning

confidence: 99%

“…e quality of the approximation will be quantified as the difference between the centers of the most distant predictive and experimental intervals in the case when they do not intersect, and the width of the intersection of the predictive and experimental intervals is the smallest, for the case of their intersection [40].…”

Section: Statement Of the Problem Of Mathematical Modeling Based On I...mentioning

confidence: 99%

“…erefore, the objective function δ(λ s ) has a global minimum only at those points for which the equality δ(λ s ) � 0 holds. Based on the theory of multiplicity of models [40], it can be stated that in the search space for solutions to the IDM structural identification task, the function δ(λ s ) has many global minima.…”

Section: Statement Of the Problem Of Mathematical Modeling Based On I...mentioning

confidence: 99%

“…Another direction in mathematical modeling according to the inductive approach is presented by the methods of mathematical modeling based on interval data [39][40][41][42][43]. e multiple estimates of the parameters of the "input-output" model, built on the results of an experiment in which the output variables are obtained in interval form, are the peculiarities of these methods [44,45].…”

Section: Introductionmentioning

confidence: 99%

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Ontology of Mathematical Modeling Based on Interval Data

et al. 2022

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An ontological approach as a tool for managing the processes of constructing mathematical models based on interval data and further use of these models for solving applied problems is proposed in this article. Mathematical models built using interval data analysis are quite effective in many applications, as they have “guaranteed” predictive properties, which are determined by the accuracy of experimental data. However, the application of mathematical modeling methods is complicated by the lack of software tools for the implementation of procedures for constructing this type of mathematical models, creating an ontological model that operates by the categories of the subject area of mathematical modeling, regardless of the modeling object proposed in this article. This approach has made it possible to generate tools for mathematical modeling of various objects based on the interval data analysis for any software development environment selected by the user. The technology of creating the software on the basis of the developed ontological superstructure for mathematical modeling using the interval data for different objects, as well as various forms of user interface implementation, is presented in this article. A number of schemes, which illustrate the technology of using the ontological approach of mathematical modeling based on interval data, are presented, and the features of its interpretation when solving environmental monitoring problems are described.

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Section: Methods Of Mathematical Modeling Based Onmentioning

confidence: 99%

Section: Statement Of the Problem Of Mathematical Modeling Based On I...mentioning

confidence: 99%

Section: Statement Of the Problem Of Mathematical Modeling Based On I...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ontology of Mathematical Modeling Based on Interval Data

et al. 2022

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“…При цьому складнішою та первинною є задача ідентифікації структури моделі, оскільки необхідно спочатку визначити базисні функції, згенерувати структуру моделі, а потім обчислити оцінки параметрів для вибору оптимальної або «кращої».. Найбільш ефективні методи структурної ідентифікації інтервальних моделей, побудовані на процедурах самоадаптації та самоорганізації за аналогією з поведінковими моделями бджолиної колонії. Для цього розв'язують складні оптимізаційні задачі [11,12,13,17]. Проте у цій статті питання структурної ідентифікації винесено за межі розгляду.…”

Section: метод побудови інтервальної моделі розподілу максимальної ам...unclassified

Interval nonlinear model of information signal amplitude distribution in the task of detection and localization of the recurrent laryngeal nerve during surgical operations

Manzhula¹,

Dyvak²

2022

Opt-el. Inf-Power Techn.

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The work proposes an approach to the construction of an interval model for solving the problems of localization of the recurrent laryngeal nerve in the process of surgery on the neck organs of the human. The interval model reflects the distribution of the amplitude of the information signal - the reaction to stimulation of the tissues of the surgery area with an alternating current of limited amplitude. The technical and software means of detection and visualization of the recurrent laryngeal nerve were analyzed. A method of identifying the specified mathematical model is proposed, which is based on the procedures for solving a nonlinear optimization problem. The proposed method simplifies the procedure for identifying the parameters of the interval model, in particular, due to the analytical representation of the objective function of the optimization problem, in contrast to the known method, where this function is discrete. The model was verified on experimental data obtained during the thyroid surgery. The developed interval nonlinear model makes it possible to detect and visualize the placement of the laryngeal nerve in the area of surgical intervention during the operation and, accordingly, ensures a reduction in the risk damage of its.

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