Fuzzy Logic-Based Software Systems

This book aims to provide information about significant advances of Fuzzy Logic in software systems to researchers, scientists, educators, students, software engineers and developers. In particular, this book explains how Fuzzy Logic, can be used in software systems to automatically predict, model, decide, diagnose, recommend etc.. In more details, Fuzzy Logic is an artificial intelligent technique that is ideal for successfully addressing, , the uncertainty, imprecision and vagueness that exist in many diverse scientific and technological areas. It was introduced by Lotfi A. Zadeh of the University of California at Berkeley, as a methodology for computing with words. This ability of Fuzzy Logic allows the representation of imprecise and vague data in a more realistic way. Therefore, Fuzzy Logic-based systems can simulate the human reasoning and decision-making processes, addressing the human subjectivity. Fuzzy Logic-based software systems are referred to any software that concerns an automated program or process that is used in everyday life, like heating or air-conditioning system, or in the scientific world, like a medical diagnostic system, which uses Fuzzy Logic in order to perform reasoning. Despite its long history, Fuzzy Logic remains an area of very active research worldwide and new applications keep on continuously emerging. Indeed, Artificial Intelligence-empowered Software Systems are constantly being developed and Fuzzy Logic is quite often their underlying empowering technology. Artificial Intelligence (AI) is the process of making machines to think and to do tasks like humans. It includes advanced technologies that make machines more intelligent and capable of performing tasks more efficiently and reliable. There is a variety of AI techniques including machine learning, artificial neural networks, deep learning, knowledge representation and reasoning techniques. An AI technique is Fuzzy Logic, which allows reasoning in cases where there is uncertainty, vagueness, imprecision and/or subjectivity. Fuzzy logic is used in smart applications and systems to make them more intelligent and allow them to reason and behave in a human-like way. Furthermore, in many circumstances fuzzy logic is combined with other AI techniques, like clustering methods, deep learning or artificial neural networks, to create more robust, reliable, effective and adaptable systems. Regarding the above, in this chapter, several fields of fuzzy logic-based software applications and the role of fuzzy logic in them are presented. Also, fuzzy c-means clustering algorithm and adaptive neuro fuzzy inference system technique are described. Artificial Intelligence (AI) is the process of making machines to think and to do tasks like humans. It includes advanced technologies that make machines more intelligent and capable of performing tasks more efficiently and reliable. There is a variety of AI techniques including machine learning, artificial neural networks, deep learning, knowledge representation and reasoning techniques. Fuzzy logic is an AI technique, which allows reasoning in cases where there is uncertainty, vagueness, imprecision and/or subjectivity. Fuzzy logic allows handling data and modeling knowledge with linguistic terms, which are called fuzzy sets. Also, it assigns several degrees of truth, which are values between 0 and 1, to each linguistic variable applying corresponding membership functions. This makes fuzzy logic a powerful tool for performing complex processes and making decisions in several real-world applications and problems, which handles uncertain, vague and/or subjective data. This is the reason why fuzzy logic is integrated in software applications and systems of several fields.