The article aims to research the application of neural networks in signal adaptive filtering. The problem of filtering signals from noise and distortion is relevant in control systems. In this paper, white noise filtering using adaptive filters and neural networks is reviewed. Neural network algorithms were chosen to solve the problem of signal filtering. Neural networks and classical adaptive filtering algorithms, such as the least mean squares and the recursive least squares, were compared considering their efficiency for additive white noise filtering tasks. A multi harmonic signal was filtered from the additive white Gaussian noise using these approaches. As a result, classical adaptive filtering algorithms demonstrated better performance in signal filtering tasks.

Although the Adaptive Cruise Control (ACC) system is a safe and beneficial driving aid, it faces several performance challenges, primarily attributable to response delays in¬curred during acceleration command calculation and execution, as well as those inherent to the controller employed. This paper focuses on investigating the response of an ACC-based Model Predictive Control (MPC) system to a lane-changing vehicle while adjusting its velocity in the presence of another vehicle in the same lane. This scenario presents a significant challenge for the system and may lead to collisions. Therefore, this paper explores methods to enhance the performance of the ACC-based MPC system in such driving scenario, employing lane change predictors such as Fine K-Nearest Neighbor (FKNN), Optimizable k-Nearest Neighbor (OKNN), Fine Gaussian Support Vector Machine (FGSVM), and Fine Decision Tree (FT).

While the Adaptive Cruise Control (ACC) system is a useful and safe driving aid, it has a number of performance issues. These are mostly caused by response delays that occur during the calculation and execution of acceleration commands, as well as issues that are specific to the controller that is being used. This paper focuses on investigating the response of an ACC-based Model Predictive Control (MPC) system to a lane-changing vehicle while adjusting its velocity in the presence of another vehicle in the same lane. This scenario presents a significant challenge for the system and may lead to collisions. Therefore, this paper explores methods to enhance the performance of the ACC-based MPC system in this driving scenario, employing lane change predictors such as Fine K-Nearest Neighbor (Fine KNN), Wide Neural Network (WNN), Fine Gaussian Support Vector Machine (Fine Gaussian SVM), and Fine Decision Tree, in both straight and curved road configurations.

The problem of estimating the parameters of deterministic signals with constant parameters is considered. The main idea is to parameterize the signal using delays and obtain a linear or nonlinear regression model. At the first stage, the signal is presented as the output signal of a linear generator of finite dimension. At the second stage, the Jordan form of the matrix and the delay operator for parameterization is applied. The performance of the algorithms considered in this article is illustrated by computer modeling.

An adaptive algorithm, compensating for unknown multiharmonic disturbance acting on linear objects under conditions of the unavailable state vector with a defined delay in the control channel is proposed. A new approach is proposed for estimating the frequencies of a multiharmonic signal. It is assumed that all parameters of the multiharmonic disturbance are unknown. The task is completed in several steps. First, an observer is constructed based on a frequency estimation scheme. Secondly, stabilization of the state of the object to zero is carried out using feedback based on the predictor. Examples are given to demonstrate the efficiency of the proposed algorithm. Our main contribution is to propose a new scheme for compensating external disturbances for a linear plant and a new approach for estimating the frequencies of a multisinusoidal signal.

In this paper, the authors examine the importance of a transdisciplinary approach to cyber-physical systems training. The article concludes that it is necessary to introduce new educational models that will contribute to the formation of innovative thinking of master students. The use of a multidisciplinary approach in the training of master students is substantiated, and a combined scheme of interdisciplinary and multidisciplinary methods is proposed on the example of the disciplines Cyber-physical systems and technologies. Specialization lies in the fact that not only the available baggage knowledge, but also ways to find their knowledge in a new application, including in non-standard conditions, readiness for self-development and improvement information. The ability of the current educational model to meet the requirements has been established.