Section Article

Application of soft computing and Fourier transform in industrial settings: new insights into the utilisation of these two technologies
Author(s): Neha Kumari

Abstract
In recent years soft computing has gained popularity and has been extensively used in many industrial applications and for solving complex real-world problems. Fuzzy logic neural networks machine learning genetic algorithms evolutionary computing and rough sets are the core components included under the field of soft computing. After providing a brief overview of the many elements of soft computing we will next analyse the hybrid form of intelligence. This essay will discuss the emerging perspectives on soft computing in Fourier transform for industrial applications. The aim of this work is to elucidate these novel perspectives. Recently there has been a surge in the amount of research being undertaken in this sector. The objective of this work is to review a substantial quantity of current publications that are relevant to the area of study and are used in the domains of industrial engineering and management. This article explores the core principles of hybrid intelligence and soft computing along with the constituent elements involved in their integration. This article provides a concise overview of soft computing techniques specifically focusing on the Fourier Transform Fast Fourier Transform and Discrete Fourier Transform. The main emphasis is on developing intelligent systems for various industrial applications. Specifically we will strive to pinpoint the most suitable domains where different combinations of intelligent methodologies may be effectively used.