A Comparative Study of Fuzzy Logic and Neural Networks for Pattern Recognition

Abstract

This study investigates the difficulties of pattern identification using fuzzy logic and neural networks.  Pattern recognition is crucial in numerous fields, including data science, computer vision, and voice recognition.  Both neural networks and fuzzy logic, the two most used methods, have their advantages and disadvantages.  Students will go extensively into neural networks and fuzzy logic after a brief review of math fundamentals.  Speed, pattern recognition, and rapid learning are the three pillars upon which the ideology of a master designer rests.  Professionals frequently employ publicly accessible datasets for the purpose of comparing various concerns.  On data classification and pattern recognition tasks, we tested neural networks and fuzzy logic for speed, accuracy, and responsiveness.  Our evaluation has combed through all the relevant research materials to assist you in making an educated selection.  Picking the right hybridization strategy is critical when working with neural networks and fuzzy logic.  Insight into patterns is one of your many strengths.  Apt algorithms excel in pattern recognition.  Neural networks as well as fuzzy logic are examples of algorithms that belong here.  Unexpected or intriguing discoveries should result from applying these tactics to this issue.