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Fuzzy Learning and Applications (International Series on Computational Intelligence)
by Marco RussoWith low computational complexity and relatively short development time, Fuzzy Logic is an indispensable tool for engineering applications. The field is growing at an unprecedented rate, and there is a need for a book that describes essential tools, applications, examples, and perspectives in the field of fuzzy learning. The editors of Fuzzy Learni
Fuzzy Linear Programming: Solution Techniques and Applications (Studies in Fuzziness and Soft Computing #379)
by Bing-Yuan Cao Ali Ebrahimnejad Seyed Hadi NasseriThis book presents the necessary and essential backgrounds of fuzzy set theory and linear programming, particularly a broad range of common Fuzzy Linear Programming (FLP) models and related, convenient solution techniques. These models and methods belong to three common classes of fuzzy linear programming, namely: (i) FLP problems in which all coefficients are fuzzy numbers, (ii) FLP problems in which the right-hand-side vectors and the decision variables are fuzzy numbers, and (iii) FLP problems in which the cost coefficients, the right-hand-side vectors and the decision variables are fuzzy numbers. The book essentially generalizes the well-known solution algorithms used in linear programming to the fuzzy environment. Accordingly, it can be used not only as a textbook, teaching material or reference book for undergraduate and graduate students in courses on applied mathematics, computer science, management science, industrial engineering, artificial intelligence, fuzzy information processes, and operations research, but can also serve as a reference book for researchers in these fields, especially those engaged in optimization and soft computing. For textbook purposes, it also includes simple and illustrative examples to help readers who are new to the field.
Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics
by Oscar Castillo Patricia MelinThis book describes recent advances on fuzzy logic augmentation of nature-inspired optimization metaheuristics and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in two main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic augmentation of nature-inspired optimization metaheuristics, which basically consists of papers that propose new optimization algorithms enhanced using fuzzy systems. The second part contains papers with the main theme of application of optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application.
Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications (Studies in Computational Intelligence #940)
by Oscar Castillo Patricia MelinWe describe in this book, recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their application in areas such as, intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing. There are some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There also some papers that presents theory and practice of meta-heuristics in different areas of application. Another group of papers describe diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical applications. There are also some papers that present theory and practice of neural networks in different areas of application. In addition, there are papers that present theory and practice of optimization and evolutionary algorithms in different areas of application. Finally, there are some papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition problems.
Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design (Studies in Computational Intelligence #1061)
by Oscar Castillo Patricia MelinThis book covers recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations. In addition, the above-mentioned methods are applied to areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. Nowadays, the main topic of the book is highly relevant, as most current intelligent systems and devices in use utilize some form of intelligent feature to enhance their performance. In addition, on the theoretical side, new and advanced models and algorithms of type-2 and type-3 fuzzy logic are presented, which are of great interest to researchers working on these areas. Also, new nature-inspired optimization algorithms and innovative neural models are put forward in the manuscript, which are very popular subjects, at this moment. There are contributions on theoretical aspects as well as applications, which make the book very appealing to a wide audience, ranging from researchers to professors and graduate students.
Fuzzy Logic and Soft Computing Applications
by Witold Pedrycz Alfredo Petrosino Vincenzo LoiaThis book comprises a selection of papers from the IFSA 2007 World Congress on theoretical advances and applications of fuzzy logic and soft computing. These papers were selected from over 400 submissions and constitute an important contribution to the theory and applications of fuzzy logic and soft computing methodologies. Soft Computing consists of several computing paradigms, including fuzzy logic, neural networks, genetic algorithms, and other techniques, which can be used to produce powerful intelligent systems for solving real-world problems. Applications range from pattern recognition to intelligent control and sow the advantages of using soft computing theory and methods. The papers of IFSA 2007 also make a contribution to this goal.
Fuzzy Logic and Technology, and Aggregation Operators: 13th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2023, and 12th International Summer School on Aggregation Operators, AGOP 2023, Palma de Mallorca, Spain, September 4–8, 2023, Proceedings (Lecture Notes in Computer Science #14069)
by Manuel González-Hidalgo Susana Montes Sebastia Massanet Daniel Ruiz-AguileraThis book constitutes the proceedings of the 13th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2023, and 12th International Summer School on Aggregation Operators, AGOP 2023, jointly held in Palma de Mallorca, Spain, during September 4–8, 2023. The 71 full papers presented in this book were carefully reviewed and selected from 161 submissions. The papers are divided into special sessions on: Interval uncertainty; information fusion techniques based on aggregation functions, preaggregation functions and their generalizations; evaluative linguistic expressions, generalized quantifiers and applications; neural networks under uncertainty and imperfect information; imprecision modeling and management in XAI systems; recent trends in mathematical fuzzy logics; fuzzy graph-based models: theory and application; new frontiers of computational intelligence for pervasive healthcare systems; fuzzy implication functions; and new challenges and ideas in statistical inference and data analysis.
Fuzzy Logic-Based Software Systems (Learning and Analytics in Intelligent Systems #34)
by Konstantina ChrysafiadiThis 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.
Fuzzy Logic: Recent Applications and Developments
by Jenny Carter Francisco Chiclana Arjab Singh Khuman Tianhua ChenSince its inception, fuzzy logic has attracted an incredible amount of interest, and this interest continues to grow at an exponential rate. As such, scientists, researchers, educators and practitioners of fuzzy logic continue to expand on the applicability of what and how fuzzy can be utilised in the real-world. In this book, the authors present key application areas where fuzzy has had significant success. The chapters cover a plethora of application domains, proving credence to the versatility and robustness of a fuzzy approach. A better understanding of fuzzy will ultimately allow for a better appreciation of fuzzy. This book provides the reader with a varied range of examples to illustrate what fuzzy logic can be capable of and how it can be applied. The text will be ideal for individuals new to the notion of fuzzy, as well as for early career academics who wish to further expand on their knowledge of fuzzy applications. The book is also suitable as a supporting text for advanced undergraduate and graduate-level modules on fuzzy logic, soft computing, and applications of AI.
Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification
by Anil Kumar A. Senthil Kumar Priyadarshi UpadhyayThis book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.
Fuzzy Management: A Fuzzy Grassroots Ontology For Online Reputation Management (essentials)
by Edy Portmann Andreas MeierAndreas Meier und Edy Portmann verwenden in diesem essential über Fuzzy Management die unscharfe Logik zur Lösung betriebswirtschaftlicher Probleme in der digitalen Wirtschaft. Die unscharfe Logik erweitert die klassische Logik mit den beiden Werten „wahr“ und „falsch“ und führt zu differenzierteren Beurteilungen. Neben Portfolio Management, Performance Measurement, Service Level Engineering und Reputationsmanagement wird die Business Intelligence mit weichen Faktoren angereichert, um den Entscheidungsprozess zu verbessern.
Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets
by Cengiz Kahraman İrem OtayThis book offers a comprehensive guide to the use of neutrosophic sets in multiple criteria decision making problems. It shows how neutrosophic sets, which have been developed as an extension of fuzzy and paraconsistent logic, can help in dealing with certain types of uncertainty that classical methods could not cope with. The chapters, written by well-known researchers, report on cutting-edge methodologies they have been developing and testing on a variety of engineering problems. The book is unique in its kind as it reports for the first time and in a comprehensive manner on the joint use of neutrosophic sets together with existing decision making methods to solve multi-criteria decision-making problems, as well as other engineering problems that are complex, hard to model and/or include incomplete and vague data. By providing new ideas, suggestions and directions for the solution of complex problems in engineering and decision making, it represents an excellent guide for researchers, lecturers and postgraduate students pursuing research on neutrosophic decision making, and more in general in the area of industrial and management engineering.
Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering
by Hongxing Li Han-Pang Huang C.L. Philip ChenAlthough fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems, especially their mathematical foundations. Fuzzy Neural Intelligent Systems fills this gap. It develops a mathematical basis for fuzzy neural networks, offers a better way of combining fuzzy logic systems with neural networks, and explores some of their engineering applications. Dividing their focus into three main areas of interest, the authors give a systematic, comprehensive treatment of the relevant concepts and modern practical applications:Fundamental concepts and theories for fuzzy systems and neural networks.Foundation for fuzzy neural networks and important related topicsCase examples for neuro-fuzzy systems, fuzzy systems, neural network systems, and fuzzy-neural systemsSuitable for self-study, as a reference, and ideal as a textbook, Fuzzy Neural Intelligent Systems is accessible to students with a basic background in linear algebra and engineering mathematics. Mastering the material in this textbook will prepare students to better understand, design, and implement fuzzy neural systems, develop new applications, and further advance the field.
Fuzzy Petri Nets for Knowledge Representation, Acquisition and Reasoning
by Hu-Chen Liu Hua ShiThis book provides valuable knowledge, useful fuzzy Petri nets (FPN) models, and practical examples that can be considered by mangers in supporting knowledge management of organizations to increase and sustain their competitive advantages. In this book, the authors proposed various improved FPN models to enhance the modeling power and applicability of FPNs in knowledge representation and reasoning. This book is useful for practitioners and researchers working in the fields of knowledge management, operation management, information science, industrial engineering, and management science. It can also be used as a textbook for postgraduate and senior undergraduate students.
Fuzzy Quantitative Management: Principles, Methodologies and Applications (Fuzzy Management Methods)
by Shaopei Lin Guohua ZhaoThis book is devoted to fuzzy quantitative studies in managerial science, discussing the philosophical background and decision-making essentials. For reference, a series of practical examples illustrate broad areas of application that are important in project risk management problems, and in complicated mega projects. Using computers to simulate human intelligence with fuzzy approaches is the basis of “Fuzzy-AI model,” which offers an efficient tool capable of simulating human intelligence in order to perform digitized decision inference and quantitative information management.
Fuzzy Recurrence Plots and Networks with Applications in Biomedicine
by Tuan D. PhamThis book presents an original combination of three well-known methodological approaches for nonlinear data analysis: recurrence, networks, and fuzzy logic.After basic concepts of these three approaches are introduced, this book presents recently developed methods known as fuzzy recurrence plots and fuzzy recurrence networks. Computer programs written in MATLAB, which implement the basic algorithms, are included to facilitate the understanding of the developed ideas. Several applications of these techniques to biomedical problems, ranging from cancer and neurodegenerative disease to depression, are illustrated to show the potential of fuzzy recurrence methods. This book opens a new door to theorists in complex systems science as well as specialists in medicine, biology, engineering, physics, computer science, geosciences, and social economics to address issues in experimental nonlinear signal and data processing.
Fuzzy Relational Mathematical Programming: Linear, Nonlinear and Geometric Programming Models (Studies in Fuzziness and Soft Computing #389)
by Bing-Yuan Cao Ji-Hui Yang Xue-Gang Zhou Zeinab Kheiri Faezeh Zahmatkesh Xiao-Peng YangThis book summarizes years of research in the field of fuzzy relational programming, with a special emphasis on geometric models. It discusses the state-of-the-art in fuzzy relational geometric problems, together with key open issues that must be resolved to achieve a more efficient application of this method. Though chiefly based on research conducted by the authors, who were the first to introduce fuzzy geometric problems, it also covers important findings obtained in the field of linear and non-linear programming. Thanks to its balance of basic and advanced concepts, and its wealth of practical examples, the book offers a valuable guide for both newcomers and experienced researcher in the fields of soft computing and mathematical optimization.
Fuzzy Rule-Based Inference: Advances and Applications in Reasoning with Approximate Knowledge Interpolation
by Qiang Shen Fangyi LiThis book covers a comprehensive approach to the development and application of a suite of novel algorithms for practical approximate knowledge-based inference. It includes an introduction to the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy inference. Collectively, this book provides a systematic tutorial and self-contained reference to recent advances in the field of fuzzy rule-based inference. Approximate reasoning systems facilitate inference by utilizing fuzzy if-then production rules for decision-making under circumstances where knowledge is imprecisely characterized. Compositional rule of inference (CRI) and fuzzy rule interpolation (FRI) are two typical techniques used to implement such systems. The question of when to apply these potentially powerful reasoning techniques via automated computation procedures is often addressed by checking whether certain rules can match given observations. Both techniques have been widely investigated to enhance the performance of approximate reasoning. Increasingly more attention has been paid to the development of systems where rule antecedent attributes are associated with measures of their relative significance or weights. However, they are mostly implemented in isolation within their respective areas, making it difficult to achieve accurate reasoning when both techniques are required simultaneously. This book first addresses the issue of assigning equal significance to all antecedent attributes in the rules when deriving the consequents. It presents a suite of weighted algorithms for both CRI and FRI fuzzy inference mechanisms. This includes an innovative reverse engineering process that can derive attribute weightings from given rules, increasing the automation level of the resulting systems. An integrated fuzzy reasoning approach is then developed from these two sets of weighted improvements, showcasing more effective and efficient techniques for approximate reasoning. Additionally, the book provides an overarching application to interpretable medical risk analysis, thanks to the semantics-rich fuzzy rules with attribute values represented in linguistic terms. Moreover, it illustrates successful solutions to benchmark problems in the relevant literature, demonstrating the practicality of the systematic approach to weighted approximate reasoning.
Fuzzy Sets Methods in Image Processing and Understanding: Medical Imaging Applications
by Isabelle Bloch Anca RalescuThis book provides a thorough overview of recent methods using higher level information (object or scene level) for advanced tasks such as image understanding along with their applications to medical images. Advanced methods for fuzzy image processing and understanding are presented, including fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. For each methodological aspect covered, illustrations from the medical imaging domain are provided. This is an ideal book for graduate students and researchers in the field of medical image processing.
Fuzzy Sets Theory Preliminary: Can A Washing Machine Think?
by Bing-Yuan Cao Hao-Ran Lin Yun-Zhang LiaoThis basic book has been used at the middle schools in Shanghai, China for more than 10 years. The book presents carefully-selected contents in order to achieve the roles of enlightenment and popularization. It mainly includes: Chapter 1: Human Brains, Computers and Fuzzy Mathematics; Chapter 2: Matrix, Fuzzy Relations and Fuzzy Matrix; Chapter 3: Fuzzy Control; Chapter 4: Fuzzy Statistics and Fuzzy Probability and Chapter 5: Fuzzy Linear Programming. It includes at the end of each chapter concise, interesting and profound reading and thinking materials, and a certain amount of exercises so as to make it an informative and interesting textbook. This book can be used not only as a textbook in senior middle schools, and in vocational colleges, but also as a primer for individually learning fuzzy mathematics.
Fuzzy Solution Concepts for Non-cooperative Games: Interval, Fuzzy and Intuitionistic Fuzzy Payoffs (Studies in Fuzziness and Soft Computing #383)
by Amit Kumar Tina VermaThis book proposes novel methods for solving different types of non-cooperative games with interval/fuzzy/intuitionistic fuzzy payoffs. It starts by discussing several existing methods and shows that some mathematically incorrect assumptions have been considered in all these methods. It then proposes solutions to adapt those methods and validate the new proposed methods, such as Gaurika method Ambika-I-IV, Mehar method and others, by using them for solving existing numerical problems. The book offers a comprehensive guide on non-cooperative games with fuzzy payoffs to both students and researchers. It provides them with the all the necessary tools to understand the methods and the theory behind them.
Fuzzy Spatiotemporal XML Data Management (Studies in Computational Intelligence #1183)
by Luyi Bai Lin ZhuSpatiotemporal data is not always precise, and fuzziness in spatiotemporal data is usually accepted because of the way the world is measured and represented. Although eXtensible Markup Language (XML) recommended by the World Wide Web Consortium (W3C) has become the de-facto standard for data representation and exchange on the Web, an edited collection of fuzzy spatiotemporal XML data management studies is still scarce. This book studies fuzzy XML approaches for spatiotemporal data management that can greatly enhance the utilization of this sort of data for spatiotemporal applications. The book is anticipated to serve as a valuable resource in this field and is expected to attract considerable interest from researchers and developers engaged in the applications within the domain of spatiotemporal data management.
Fuzzy Stochastic Optimization
by Shuming Wang Junzo WatadaCovering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies. The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins by outlining the history and development of the fuzzy random variable before detailing numerous optimization models and applications that include the design of system controls for a dam.
Fuzzy System Identification and Adaptive Control (Communications and Control Engineering)
by Bin Jiang Ruiyun Qi Gang TaoThis book provides readers with a systematic and unified framework for identification and adaptive control of Takagi–Sugeno (T–S) fuzzy systems. Its design techniques help readers applying these powerful tools to solve challenging nonlinear control problems. The book embodies a systematic study of fuzzy system identification and control problems, using T–S fuzzy system tools for both function approximation and feedback control of nonlinear systems. Alongside this framework, the book also: introduces basic concepts of fuzzy sets, logic and inference system; discusses important properties of T–S fuzzy systems; develops offline and online identification algorithms for T–S fuzzy systems; investigates the various controller structures and corresponding design conditions for adaptive control of continuous-time T–S fuzzy systems; develops adaptive control algorithms for discrete-time input–output form T–S fuzzy systems with much relaxed design conditions, and discrete-time state-space T–S fuzzy systems; and designs stable parameter-adaptation algorithms for both linearly and nonlinearly parameterized T–S fuzzy systems. The authors address adaptive fault compensation problems for T–S fuzzy systems subject to actuator faults. They cover a broad spectrum of related technical topics and to develop a substantial set of adaptive nonlinear system control tools. Fuzzy System Identification and Adaptive Control helps engineers in the mechanical, electrical and aerospace fields, to solve complex control design problems. The book can be used as a reference for researchers and academics in nonlinear, intelligent, adaptive and fault-tolerant control.
Fuzzy Techniques: Proceedings of the 2019 Joint World Congress of the International Fuzzy Systems Association and the Annual Conference of the North American Fuzzy Information Processing Society IFSA/NAFIPS'2019 (Lafayette, Louisiana, USA, June 18–21, 2019) (Advances in Intelligent Systems and Computing #1000)
by Vladik Kreinovich Martine Ceberio Marek Reformat Ildar Batyrshin Ralph Baker KearfottThis book describes the latest findings related to fuzzy techniques, discussing applications in control, economics, education, humor studies, industrial engineering, linguistics, management, marketing, medicine and public health, military engineering, robotics, ship design, sports, transportation, and many other areas. It also presents recent fuzzy-related algorithms and theoretical results that can be used in other application areas. Featuring selected papers from the Joint World Congress of the International Fuzzy Systems Association (IFSA) and the Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) IFSA-NAFIPS’2019, held in Lafayette, Louisiana, USA, on June 18–21, 2019, the book is of interest to practitioners wanting to use fuzzy techniques to process imprecise expert knowledge. It is also a valuable resource for researchers wishing to extend the ideas from these papers to new application areas, for graduate students and for anyone else interested in problems involving fuzziness and uncertainty.