штучні нейронні мережі

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.

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.

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.

The article develops an intelligent system of analysis and neural network forecasting of battery charge consumption for automated vehicles (AGVs). For this purpose, the types of AGV and the methods of effective forecasting of their battery charge consumption were analyzed. It is established that they are based on optimal robot control processes; application of technologies to increase capacity and extend service life.

The article presents the results of the study of the possibilities of using neural networks to solve the problem of determining the active set of a wind farm (WF), taking into account the efficiency of each wind turbines (WT).

The automated translation, speech recognition and synthesis, object detection as well as emotion recognition are well known complex tasks that modern smartphone can solve. It became possible with intensive usage of algorithms of Artificial Intelligence and Machine Learning. Most popular now are implementations of deep neural networks and deep learning algorithms. Such algorithms are widely used in all verticals and need hardware accelerators as well as deep cooperation between both software and hardware parts.

This pa­per addres­ses the prob­lem of iden­tif­ying rhe­olo­gi­cal pa­ra­me­ters of wo­od using ar­ti­fi­ci­al neu­ral net­works with pa­ral­lel le­ar­ning al­go­rithm using Python prog­ram­ming lan­gua­ge, Cha­iner fra­me­work and CU­DA techno­logy. An in­tel­li­gent system for iden­ti­fi­ca­ti­on of rhe­olo­gi­cal pa­ra­me­ters of wo­od has be­en de­ve­lo­ped. The system cre­ated con­ta­ins the most user-fri­endly in­ter­fa­ce, all the ne­ces­sary set of to­ols for au­to­ma­ti­on of the pro­cess of vis­ua­li­za­ti­on and analysis of da­ta.

The problems of information processing in solving the technological preparation of production were considered. For this purpose use the effective methods of multivariate statistical analysis and artificial neural networks. Compression algorithms in the original array of information by factor analysis methods, component analysis and multidimensional scaling, classification algorithms and pattern recognition methods of discriminate and cluster analysis, as well as algorithms for modeling of group account of arguments and artificial neural networks were implemented with software.

The purpose of research. The main purpose of research is to analyze the relief of  various surfaces. For example, to select on the surface the individual sections of a certain  form, such as slopes that are oriented in a given direction. The main aim of the article is the use of artificial neural networks (ANN). To solve the problem of classification a binary classifier was created and its work and its accuracy was studied. Method. The research was carried out on the certain section of the earth's surface. The digital model, presented by greed file, was created.