Neural Networks, Fuzzy Systems and Pattern Recognition: A Comparative Study

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have been rapidly advancing in recent years, with many new techniques and models being developed. One area of AI and ML that has more focuses on Pattern Recognition (PR). PR is a subfield of ML that deals with the identification and classification of patterns in data. This field is closely related to other subfields of AI and ML, such as Neural Networks (NNs) and Neuro-Fuzzy Systems (NFS). NNs are a kind of artificial intelligence inspired by the way our brains work. This paper will provide a comparative research of three fields: Neural Networks (NNs), Neuro-Fuzzy Systems (NFS) and Pattern Recognition (PR), highlighting their similarities and differences. NNs, NFS, and PR are three closely related fields of research in the field of AI and ML. The paper begins with a brief introduction to each of these fields, followed by a discussion of their similarities and differences. NNs are a type of AI that are modeled after the function and structure of the human brain system. They integrate a wide-range of interlinked processing nodes, known as neurons that are used to perform various tasks such as PR and control. NNs are particularly useful for tasks that involve large amounts of data, such as image and speech recognition.