Study of algorithmic approaches to digital signal filtering and the influence of input quantization on output accuracy

This article explores the design and application of intelligent control systems tailored for industrial and production environments, with a particular focus on integrating advanced digital filtering and noise reduction strategies. Traditional adaptive control systems often struggle to maintain flexibility and responsiveness in uncertain and dynamic conditions. In response, the study investigates the potential of intelligent control architectures, enhanced by neural networks and fuzzy logic, to overcome these limitations. A comprehensive comparison is made between classical and modern algorithmic approaches to digital filtering, highlighting the critical role of software-based solutions in suppressing quantization errors and mitigating the influence of random noise in analogue sensor data. The implementation of both recursive and non-recursive digital filter models is addressed through digital signal processing techniques, enabling real-time correction and analysis of incoming signals. Using mathematical tools such as Z-transforms and transfer functions, the paper provides an in-depth performance evaluation of filter behaviour under both deterministic and stochastic inputs. The research supports the broader integration of AI-driven technologies in modern automation systems, paving the way for more adaptive, efficient, and fault-tolerant control mechanisms in complex environments.