This study focuses on the analysis of the process of adjusting weights in neural networks, highlighting key aspects such as the influence of the error vector and the activity of previous layers on weight changes. The work proposes methods that allow for effective management of activity increment vectors and ensures their alignment along the error vector direction. Special attention is given to threshold-adjusted mapping and the algorithm for vector orthogonalization, which contribute to improving approximation accuracy and increasing learning speed, which is important for control tasks. The results of the study emphasize the importance of accurately determining error vectors and suggest promising directions for further research in the field of machine learning.