artificial neural networks

Predictive Control Using Artificial Neural Networks for Nonlinear Epidemic Models

Mathematical biology offers powerful tools for predicting epidemic dynamics and guiding interventions.  However, the complexity of these systems often makes it difficult to derive explicit control strategies.  This study proposes the application of artificial neural networks to nonlinear systems with or without a known analytical expression for the control by learning and reproducing control mechanisms from labeled data.

Synthesis of Parallel-Pipeline Devices for Vertical Computation of Basic Multi-Operand Neural Operations

This paper presents a study of hardware implementation of basic multi-operand neural ope- rations for artificial neural networks. The operational basis of neural networks is identified, which includes groups of preprocessing operations, processor operations, and transfer function computations. The selection of basic multi-operand neural operations is justified: finding extreme values in one- dimensional arrays, calculating the sum of squared differences, scalar product computation, and group summation.

The Role of Artificial Intelligence in Enhancing Emotional Intelligence Competencies of Civil Servants

The article examines the role of artificial intelligence in improving the emotional and intellec- tual competencies of civil servants and assesses its impact on the quality of managerial decisions and communication processes in the public sector. The relevance of the study is driven by the need to adapt public administration personnel to the challenges of digital transformation, requiring enhanced com- munication skills, stress resistance, and the ability to make well-balanced decisions.

Neural network models with different input: An application on stock market forecasting

It is no doubt challenging to forecast the stock market accurately in reality due to the ever-changing market.  Ever since Artificial Neural Networks (ANNs) have been recognized as universal approximators, they are extensively used in forecasting albeit not having a systematic approach in identifying optimal input.  The appropriate number of significant lags of a time series corresponds to the optimal input in time series forecasting.  Hence, this study aims to compare the effect of several approaches in determining the input lag for ANNs prior to stock market forecasti

Development a Multifactorial Portrait Model of Software Complexes’ Supporting Subjects, Using Artificial Neural Networks

Impact factors, that are shaping the individualistic perception of the supported objects by the relevant subjects, who interact with them, directly or indirectly, are considered in this research. A form of impact factors’ (performing impact on the supported software complexes) representation has been studied and proposed.

Самоорганізація в моделях обчислень нейронних мереж і декларативних програм

Порівнюються дві моделі обчислень – штучні нейронні мережі та декларативні програми, що побудовані на основі логіки предикатів. Пропонуються такі їх узагальнення, за яких процес обчислень зможе приводити до цілеспрямованих змін власної програми. Сформульовано принципи самоорганізації, за якими вказані зміни будуть не хаотичними, а визначеними в результаті пошуку. Для детального вивчення самоорганізації необхідне зближення та взаємне доповнення розглянутих моделей.

Simultaneous surrogate modeling and dimension reduction using unsupervised learning. Application to parametric wing shape optimization

This paper presents a machine-learning-based approach that enables simultaneous surrogate modeling and dimension reduction and applies it to aerodynamic parametric shape optimization.  Aerodynamic shape optimization is a crucial process in various industries, including aerospace, automotive, and renewable energy.  It involves iteratively improving the properties of a system by evaluating an objective function and driving its minimization or maximization using an optimization algorithm.  However, the evaluation of aerodynamic objective functions requires computationally

Robust shape optimization using artificial neural networks based surrogate modeling for an aircraft wing

Aerodynamic shape optimization is a very active area of research that faces the challenges of highly demanding Computational Fluid Dynamics (CFD) problems, optimization with Partial Differential Equations (PDEs) as constraints, and the appropriate treatment of uncertainties.  This includes the development of robust design methodologies that are computationally efficient while maintaining the desired level of accuracy in the optimization process.  This paper addresses aerodynamic shape optimization problems involving uncertain operating conditions.  After a review of pos

Backpropagation algorithm for complex neural networks

Розглянуто комплексні штучні нейронні мережі, функції активації яких є комп- лексними аналогами раціональної сигмоїди. Наведено алгоритм навчання цих мереж, заснований на методі зворотного поширення похибки.

Neural networks with complex weights and continuously differentiable activation function have been studied in the paper. Learning algorithm based on the backpropagation method for rational sigmoid function has been given in the paper.

Технологія нейрокомп’ютингу реального часу

Проаналізовано особливості апаратної реалізації штучних нейронних мереж, вибрано принципи побудови, визначено шляхи підвищення ефективності використання обладнання, розроблено методи синтезу та базові структури нейрокомп’ютерних систем реального часу.

Features of hardware representation of artificial neural networks were analyzed, principles of construction were chosen, ways of efficiency increase of equipment use were determined, methods of synthesis and base structures of the neural computing, , real-time systems were developed.