The work is devoted to the development of the radial basis functions (RBF networks) neural network new architecture – a polymorphic RBF network in which the one-dimensional radial basis functions (RBFs) in the hidden layer instead, multidimensional RBFs are used, which makes it possible to better approximate complex functions that depend on several independent variables. Moreover, in its second layer, the summing the RBF outputs one by one from each group instead, multiplication is used, which allows the polymorphic RBF network to better identify relations between independent variables. Based on the training classical RBF networks evolutionary algorithm, the polymorphic RBF network training algorithm was created, which, through the initializing weight coefficients methods use taking into account the tasks structure and preliminary values, using the mutations tournament selection, adding additional criteria to the fitness function to take into account stability and speed training a polymorphic RBF network, as well as using an evolutionary mutation strategy, allowed us to obtain the lowest errors in training and testing a polymorphic RBF network compared to known RBF network architectures. The created polymorphic RBF network practical application possibility is demonstrated experimentally using the helicopters turboshaft engines (using the example, the TV3-117 turboshaft engine) operating process parameters optimizing solving task using a multicriteria optimization algorithm. The optimal Pareto front was obtained, which made it possible to obtain the engine operation three additional modes: maximum reduction of specific fuel consumption at the total pressure in the compressor increase degree increased value by 5.0 %, specific fuel consumption minimization at the total pressure in the compressor increase degree reduced value by 1.0 %, the total pressure in the compressor increases degree optimal value with a slight increase in specific fuel consumption by 10.5 %. Future research prospects include adapting the developed methods and models into the general concept for monitoring and controlling helicopter turboshaft engines during flight operations. This concept is implemented in the neural network expert system and the on-board automatic control system.

This work focuses on developing a universal onboard neural network system for restoring information when helicopter turboshaft engine sensors fail. A mathematical task was formulated to determine the occurrence and location of these sensor failures using a multi-class Bayesian classification model that incorporates prior knowledge and updates probabilities with new data. The Bayesian approach was employed for identifying and localizing sensor failures, utilizing a Bayesian neural network with a 4–6–3 structure as the core of the developed system. A training algorithm for the Bayesian neural network was created, which estimates the prior distribution of network parameters through variational approximation, maximizes the evidence lower bound of direct likelihood instead, and updates parameters by calculating gradients of the log-likelihood and evidence lower bound, while adding regularization terms for warnings, distributions, and uncertainty estimates to interpret results. This approach ensures balanced data handling, effective training (achieving nearly 100% accuracy on both training and validation sets), and improved model understanding (with training losses not exceeding 2.5%). An example is provided that demonstrates solving the information restoration task in the event of a gas-generator rotor r.p.m. sensor failure in the TV3-117 helicopter turboshaft engine. The developed onboard neural network system implementing feasibility on a helicopter using the neuro-processor Intel Neural Compute Stick 2 has been analytically proven.

The purpose of the research is to develop mathematical models, solution methods and layouts of tools for problems solving of integrating information resources and creation of intelligent systems of business analytics based on effective models. These problems can be solved by automating the business processes execution and introducing artificial intelligence components into the business processes management systems. It can be said that the essence of the modern stage of the business processes modelling systems development is the transition from mainly manual (or with the use of auxiliary software) methods of business processes analysis to mainly automatic management of the business processes execution, construction of intelligent business processes networks in the interconnected conceptual models’ set form that encapsulate knowledge about the structure, the business processes features, system events, limitations and dependencies and are processed by machine. Decision-making powers are delegated to such information system in clearly defined (most often simple, routine) situations. So, in this way, it is possible to form the information resource of intelligent systems of business analytics as a single coherent set of data, suitable for use in solving a wide range of multifaceted problems. The integration approach of forming information resources has certain advantages over other approaches, in particular, regarding the information resources of intelligent systems of business analytics. The use of integration as a means of forming a set of consistent data has certain advantages, namely, it allows: combine data of different formats, content and origins in a single, consistent set; combine data without converting them to a single format, which is especially important when such conversion is difficult or impossible; creates virtual custom images of data that do not depend on their real appearance; creates opportunities to operate both real physical and virtual data in their combination; dynamically supplement, change and transform both the data itself and their descriptions; to provide uniform methods and technologies of perception and application of a large amount of various data.

In the modern world, the military sphere occupies a very high place in the life of the country. At the same time, this area needs quick and accurate solutions. This decision can greatly affect the unfolding of events on the battlefield and indicate that they must be used carefully, using all possible means. During the war, the speed and importance of decisions are very important, and we note that the relevance of this topic is growing sharply. The purpose of the work is to create a comprehensive information system that facilitates the work of commanders of tactical units, which organizes the visualization and classification of aerial objects in real-time, the classification of objects for radio-technical intelligence, the structuring of military information and facilitates the perception of military information. The object of research/development is a phenomenon that creates a problematic problem, has the presence of slowing factors in the process of command and control, using teams of tactical links, which can slow down decision-making, as well as affect their correctness. The research/development aims to address emerging bottlenecks in the command-and-control process performed by tactical link teams, providing improved visualization, analysis and work with military data. The result of the work is an information system for processing military data to help commanders of tactical units. This system significantly improves on known officer assistance tools, although it includes a set of programs that have been used in parallel on an as-needed basis. Using modern information technologies and ease of use, the system covers problems that may arise for commanders. Also, each program included in the complex information system has its degree of innovation. The information system for structuring military information is distinguished by the possibility of use on any device. The information system for the visualization and clustering of aerial objects and the information system for the classification of objects for radio technical intelligence are distinguished by their component nature. This means that the application can use sources of input information and provides an API to use other processing information. Regarding the information system for integration into information materials, largely unknown terms and abbreviations are defined, so such solutions, cannot integrate the required data into real documents. Therefore, using this comprehensive information system, the command of tactical units will have the opportunity to improve the quality and achieve the command-and-control process.

The article develops technology for generating song lyrics extensions using large language models, in particular the T5 model, to speed up, supplement, and increase the flexibility of the process of writing lyrics to songs with/without taking into account the style of a particular author. To create the data, 10 different artists were selected, and then their lyrics were selected. A total of 626 unique songs were obtained. After splitting each song into several pairs of input-output tapes, 1874 training instances and 465 test instances were obtained. Two language models, NSA and SA, were retrained for the task of generating song lyrics. For both models, t5-base was chosen as the base model. This version of T5 contains 223 million parameters. The analysis of the original data showed that the NSA model has less degraded results, and for the SA model, it is necessary to balance the amount of text for each author. Several text metrics such as BLEU, RougeL, and RougeN were calculated to quantitatively compare the results of the models and generation strategies. The value of the BLEU metric is the most diverse, and its value varies significantly depending on the strategy. At the same time, Rouge metrics have less variability and a smaller range of values. In total, for comparison, we used 8 different decoding methods for text generation supported by the transformers library, including Greedy search, Beam search, Diverse beam search, Multinomial sampling, Beam-search multinomial sampling, Top-k sampling, Top-p sampling, and Contrastive search. All the results of the lyrics comparison show that the best method for generating lyrics is beam search and its variations, including ray sampling. The contrastive search usually outperformed the usual greedy approach. The top-p and top-k methods do not have a clear advantage over each other, and in different situations, they produced different results.

The article develops a technology for finding tweet trends based on clustering, which forms a data stream in the form of short representations of clusters and their popularity for further research of public opinion. The accuracy of their result is affected by the natural language feature of the information flow of tweets. An effective approach to tweet collection, filtering, cleaning and pre-processing based on a comparative analysis of Bag of Words, TF-IDF and BERT algorithms is described. The impact of stemming and lemmatization on the quality of the obtained clusters was determined. Stemming and lemmatization allow for significant reduction of the input vocabulary of Ukrainian words by 40.21% and 32.52% respectively. And optimal combinations of clustering methods (K-Means, Agglomerative Hierarchical Clustering and HDBSCAN) and vectorization of tweets were found based on the analysis of 27 clustering of one data sample. The method of presenting clusters of tweets in a short format is selected. Algorithms using the Levenstein Distance, i.e. fuzz sort, fuzz set and Levenshtein, showed the best results. These algorithms quickly perform checks, have a greater difference in similarities, so it is possible to more accurately determine the limit of similarity. According to the results of the clustering, the optimal solutions are to use the HDBSCAN clustering algorithm and the BERT vectorization algorithm to achieve the most accurate results, and to use K-Means together with TF-IDF to achieve the best speed with the optimal result. Stemming can be used to reduce execution time. In this study, the optimal options for comparing cluster fingerprints among the following similarity search methods were experimentally found: Fuzz Sort, Fuzz Set, Levenshtein, Jaro Winkler, Jaccard, Sorensen, Cosine, Sift4. In some algorithms, the average fingerprint similarity reaches above 70%. Three effective tools were found to compare their similarity, as they show a sufficient difference between comparisons of similar and different clusters (> 20%).
The experimental testing was conducted based on the analysis of 90,000 tweets over 7 days for 5 different weekly topics: President Volodymyr Zelenskyi, Leopard tanks, Boris Johnson, Europe, and the bright memory of the deceased. The research was carried out using a combination of K-Means and TF-IDF methods, Agglomerative Hierarchical Clustering and TF-IDF, HDBSCAN and BERT for clustering and vectorization processes. Additionally, fuzz sort was implemented for comparing cluster fingerprints with a similarity threshold of 55%. For comparing fingerprints, the most optimal methods were fuzz sort, fuzz set, and Levenshtein. In terms of execution speed, the best result was achieved with the Levenshtein method. The other two methods performed three times worse in terms of speed, but they are nearly 13 times faster than Sift4. The fastest method is Jaro Winkler, but it has a 19.51% difference in similarities. The method with the best difference in similarities is fuzz set (60.29%). Fuzz sort (32.28%) and Levenshtein (28.43%) took the second and third place respectively. These methods utilize the Levenshtein distance in their work, indicating that such an approach works well for comparing sets of keywords. Other algorithms fail to show significant differences between different fingerprints, suggesting that they are not adapted to this type of task.

Time-frequency and time dependence of the output signal morphology of nonlinear oscillator neuron based on Van der Pol model using analytical and numerical methods were investigated. Threshold effect neuron, when it is exposed to external non-stationary signals that vary in shape, frequency and amplitude was considered.