Abstract

Industrial applications increasingly rely on advanced computational techniques to enhance efficiency accuracy and reliability in production monitoring and control processes. Soft computing and Fourier transform methodologies have emerged as pivotal tools in this context offering complementary capabilities for handling uncertainty nonlinearity and frequency-domain analysis. This research explores the integration of soft computing methods—including fuzzy logic genetic algorithms and neural networks—with Fourier transform-based techniques to provide innovative solutions for industrial challenges. The study investigates theoretical underpinnings algorithmic frameworks implementation strategies and performance evaluation across a variety of industrial applications such as signal processing predictive maintenance fault diagnosis and process optimization. By reviewing existing literature designing hybrid computational models and conducting experimental analyses the research demonstrates that