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.

In this article dynamically positioned surface vessel model is considered. To achieve precise performance of the vessel with unknown parameters two control problem was solved. First one is online parametric identification. The estimation of unknown parameters of the surface vessel model is obtained by methods, which are gradient descent method, extended Kalman filter and dynamic regressor extension and mixing (DREM). Second problem is the design of the adaptive control based on the collected results from estimators. For this purposes inverse dynamics control is considered. The convergence of the system is shown in the modeling section. DREM estimation method showed best results.