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The number of fuzzy logic applications is very large. This book tells the reader how to use fuzzy logic to find solutions in areas such as control systems, factory automation, product quality control, product inspection, instrumentation, pattern recognition, image analysis, database query processing, decision support, data mining, time series (waveform) databases, geographic information systems, and image databases. Those who have applications in these areas will find the book invaluable.The author was the first student to write a PhD fuzzy logic thesis under Professor Lotfi A Zadeh (the inventor of fuzzy logic), in 1967 at the University of California, Berkeley. In 1993, he designed and introduced the NICEL language for writing fuzzy programs that enclose if-then rules. NICEL is powerful and easy to use. The reader will find in the book that many algorithms for real world applications can be conveniently represented in NICEL.
This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to “teach” the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, as well as disciplines including astronomy, biology, medicine and earth sciences. Software developers may also benefit from this book, which is intended as both an introductory textbook and self-study reference guide to fuzzy logic and its applications. The complete set of functions that make up the Fuzzy LISP programming toolbox can be downloaded from a companion book’s website.
Traces the story of Lofti Zadeh, an Iranian-American professor at Berkeley who began developing fuzzy logic - the way to program computers so they can mimic the imprecise way that humans make decisions.
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. A Fuzzy Logic-based system consists of three basic modules: Fuzzifier, Inference Engine and Defuzzifier. The Fuzzifier accepts as input numerical data and assigns them to fuzzy sets with some degree of membership, converting crisp data to fuzzy sets. The Inference Engine applies fuzzy rules over the defined fuzzy sets and produces outputs based on linguistic information. The Defuzzifier, converts fuzzy values into crisp values. The use of Fuzzy Logic in software systems constitutes a compelling and active research area in recent years, especially due to the increased interest in artificial intelligence. In the view of the above, this book presents thoroughly the Fuzzy Logic theory and the structure and operation of a Fuzzy Logic-based system. It also explains the role of Fuzzy Logic in artificial intelligence and smart applications, presenting how it can improve the efficiency and effectiveness of automatic processes and tasks. Furthermore, the book describes techniques of artificial intelligence with which the fuzzy logic is combined and how. Furthermore, this book presents several Fuzzy Logic-based software systems in the discipline of medicine, education, decision making and recommendation, natural language processing, automotive engineering and industry, heating, ventilation and air-conditioning, navigation, scheduling, network traffic and security. Thereby, this book can provide deep insights and valuable information not only to readers of computer science-related disciplines, but also to readers, who come from a variety of disciplines and are interesting in systems that perform tasks related to their discipline, in a more efficient way.
This book provides a broad-ranging, but detailed overview of the basics of Fuzzy Logic. The fundamentals of Fuzzy Logic are discussed in detail, and illustrated with various solved examples. The book also deals with applications of Fuzzy Logic, to help readers more fully understand the concepts involved. Solutions to the problems are programmed using MATLAB 6.0, with simulated results. The MATLAB Fuzzy Logic toolbox is provided for easy reference.
Fuzzy logic has virtually exploded over the landscape of emerging technologies, becoming an integral part of myriad applications and a standard tool for engineers. Until recently, most of the attention and applications have centered on fuzzy systems implemented in software. But these systems are limited. Problems that require real-time operation, low area, or low power consumption demand hardware designed to the fuzzy paradigm - and engineers with the background and skills to design it. Microelectronic Design of Fuzzy Logic-Based Systems offers low-cost answers to issues that software cannot resolve. From the theoretical, architectural, and technological foundation to design tools and applications, it serves as your guide to effective hardware realizations of fuzzy logic. Review fuzzy logic theory and the basic issues of fuzzy sets, operators, and inference mechanisms Explore the trade-offs between efficient theoretical behavior and practical hardware realizations Discover the properties of the possible microelectronic realizations of fuzzy systems - conventional processors, fuzzy coprocessors, and fuzzy chips Investigate the design of fuzzy chips that implement the whole fuzzy inference method into silicon Analyze analog, digital, and mixed-signal techniques Reduce your design effort for fuzzy systems with CAD tools - learn the requirements they should meet and survey current environments. Put it all together - see examples and case studies illustrating how all of this is used to solve particular problems related to control and neuro-fuzzy applications
Technology has dramatically changed the way in which knowledge is shared within and outside of traditional classroom settings. The application of fuzzy logic to new forms of technology-centered education has presented new opportunities for analyzing and modeling learner behavior. Fuzzy Logic-Based Modeling in Collaborative and Blended Learning explores the application of the fuzzy set theory to educational settings in order to analyze the learning process, gauge student feedback, and enable quality learning outcomes. Focusing on educational data analysis and modeling in collaborative and blended learning environments, this publication is an essential reference source for educators, researchers, educational administrators and designers, and IT specialists. This premier reference monograph presents key research on educational data analysis and modeling through the integration of research on advanced modeling techniques, educational technologies, fuzzy concept maps, hybrid modeling, neuro-fuzzy learning management systems, and quality of interaction.
Prolog for logic programming is one of the most intensively studied software languages in the 1980s. During the same period, the data-flow model for parallel computation attracted a lot of attention of researchers in the computer science; hence, it was very natural that several approaches were tried toward combining the two and implementing logic programs in parallel machines with the data-flow architecture. These approaches, however, were rather indirect ones in the sense that they developed programs describing AND/OR-parallelism for deduction using a data-flow language and executed them in a data-flow computer, and yet did not devise a direct' model for parallel execution (reasoning) of a logic program. This book discusses fuzzy logic inferencing for Pong; dislog; SEProlog; and provides direct graphical representations of first-order logic for inference.
This volume constitutes the thoroughly refereed post-workshop proceedings of an international workshop on fuzzy logic in Artificial Intelligence held in Negoya, Japan during IJCAI '97. The 17 revised full papers presented have gone through two rounds of reviewing and revision. Three papers by leading authorities in the area are devoted to the general relevance of fuzzy logic and fuzzy sets to AI. The remaining papers address various relevant issues ranging from theory to application in areas like knowledge representation, induction, logic programming, robotics, pattern recognition, etc.