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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 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 with Engineering Applications
by Timothy J. RossFuzzy Logic with Engineering Applications, Fourth Edition Timothy J. Ross, University of New Mexico, USA The latest update on this popular textbook The importance of concepts and methods based on fuzzy logic and fuzzy set theory has been rapidly growing since the early 1990s and all the indications are that this trend will continue in the foreseeable future. Fuzzy Logic with Engineering Applications, Fourth Edition is a new edition of the popular textbook with 15% of new and updated material. Updates have been made to most of the chapters and each chapter now includes new end-of-chapter problems. Key features: New edition of the popular textbook with 15% of new and updated material. Includes new examples and end-of-chapter problems. Has been made more concise with the removal of out of date material. Covers applications of fuzzy logic to engineering and science. Accompanied by a website hosting a solutions manual and software. The book is essential reading for graduates and senior undergraduate students in civil, chemical, mechanical and electrical engineering as wells as researchers and practitioners working with fuzzy logic in industry.
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 Mathematics: A Fundamental Introduction (Synthesis Lectures on Mathematics & Statistics)
by Apostolos SyropoulosThis book aims to introduce readers without a strong mathematical background to the basic ideas of fuzzy set theory and logic. Fuzzy mathematics is the mathematics of vagueness, a universal property of this world. There are many objects that are called vague because they cannot be precisely defined. Since vagueness is so common, a tool is needed to describe it and to effectively deal with it. Fuzzy mathematics is such a tool, and it is used by most researchers and scholars. As such, this book provides a short overview of the field written for non-specialists. This book allows readers to delve into the theory of fuzzy sets and introduces core mathematical ideas without using the usual formalities of books in mathematics, i.e. theorems, proofs, etc.
Fuzzy Models in Economics (Studies in Fuzziness and Soft Computing #402)
by Gorkhmaz ImanovThis book offers a timely guide to fuzzy methods applied to the analysis of socioeconomic systems. It provides readers with a comprehensive and up-to-date overview of the algorithms, including the theory behind them, as well as practical considerations, current limitations and solutions. Each chapter focuses on a different economic problem, explaining step by step the process to approach it, using the corresponding fuzzy tools. The book covers elements of intuitionistic fuzzy logics, fuzzy entropy and the fuzzy DEMATEL method, a fuzzy approach to calculate the financial stability index. It also reports on some new models of social, financial and ecological security, and on a novel fuzzy method for evaluating the quality of development of information economy.
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 Multicriteria Decision-Making
by Petr Ekel Roberta Parreiras Witold PedryczFuzzy Multicriteria Decision-Making: Models, Algorithms and Applications addresses theoretical and practical gaps in considering uncertainty and multicriteria factors encountered in the design, planning, and control of complex systems. Including all prerequisite knowledge and augmenting some parts with a step-by-step explanation of more advanced concepts, the authors provide a systematic and comprehensive presentation of the concepts, design methodology, and detailed algorithms. These are supported by many numeric illustrations and a number of application scenarios to motivate the reader and make some abstract concepts more tangible.Fuzzy Multicriteria Decision-Making: Models, Algorithms and Applications will appeal to a wide audience of researchers and practitioners in disciplines where decision-making is paramount, including various branches of engineering, operations research, economics and management; it will also be of interest to graduate students and senior undergraduate students in courses such as decision making, management, risk management, operations research, numerical methods, and knowledge-based systems.
Fuzzy Multiple Objective Decision Making
by Gwo-Hshiung Tzeng Jih-Jeng HuangMulti-objective programming (MOP) can simultaneously optimize multi-objectives in mathematical programming models, but the optimization of multi-objectives triggers the issue of Pareto solutions and complicates the derived answers. To address these problems, researchers often incorporate the concepts of fuzzy sets and evolutionary algorithms into M
Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering
by Hongxing Li C.L. Philip Chen Han-Pang HuangAlthough 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 Optimization Techniques in the Areas of Science and Management (Computational Intelligence in Engineering Problem Solving)
by Santosh Kumar Das Massimiliano GiacaloneThis book helps to enhance the application of fuzzy logic optimization in the areas of science and engineering. It includes implementation and modeling paradigms such as path planning and routing design for different wireless networks, organization behavior strategies modeling, and so forth. It also: Explains inventory control management, uncertainties management, loss minimization, game optimization, data analysis and prediction, and different decision-making system and management, and so forth. Describes applicability of fuzzy optimization techniques in areas of science and management. Resolves several issues based on uncertainty using member function. Helps to map different problems based on mathematical modelling. Includes issues and problems based on linear and non-linear optimizations. Focuses on management science such as manpower management and inventory planning. This book is aimed at researchers and graduate students in signal processing, power systems, systems and industrial engineering, and computer networks.
Fuzzy Petri Nets for Knowledge Representation, Acquisition and Reasoning
by Hua Shi Hu-Chen LiuThis 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 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 Modeling with Applications to Geophysical, Biological, and Engineering Systems (Systems Engineering Ser.)
by Lucien Duckstein Andras BardossyThis book presents in a systematic and comprehensive manner the modeling of uncertainty, vagueness, or imprecision, alias "fuzziness," in just about any field of science and engineering. It delivers a usable methodology for modeling in the absence of real-time feedback.The book includes a short introduction to fuzzy logic containing basic definitions of fuzzy set theory and fuzzy rule systems. It describes methods for the assessment of rule systems, systems with discrete response sets, for modeling time series, for exact physical systems, examines verification and redundancy issues, and investigates rule response functions.Definitions and propositions, some of which have not been published elsewhere, are provided; numerous examples as well as references to more elaborate case studies are also given. Fuzzy rule-based modeling has the potential to revolutionize fields such as hydrology because it can handle uncertainty in modeling problems too complex to be approached by a stochastic analysis. There is also excellent potential for handling large-scale systems such as regionalization or highly non-linear problems such as unsaturated groundwater pollution.
Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set
by Tamalika ChairaProvides detailed mathematical exposition of the fundamentals of fuzzy set theory, including intuitionistic fuzzy sets This book examines fuzzy and intuitionistic fuzzy mathematics and unifies the latest existing works in literature. It enables readers to fully understand the mathematics of both fuzzy set and intuitionistic fuzzy set so that they can use either one in their applications. Each chapter of Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set begins with an introduction, theory, and several examples to guide readers along. The first one starts by laying the groundwork of fuzzy/intuitionistic fuzzy sets, fuzzy hedges, and fuzzy relations. The next covers fuzzy numbers and explains Zadeh's extension principle. Then comes chapters looking at fuzzy operators; fuzzy similarity measures and measures of fuzziness; and fuzzy/intuitionistic fuzzy measures and fuzzy integrals. The book also: discusses the definition and properties of fuzzy measures; examines matrices and determinants of a fuzzy matrix; and teaches about fuzzy linear equations. Readers will also learn about fuzzy subgroups. The second to last chapter examines the application of fuzzy and intuitionistic fuzzy mathematics in image enhancement, segmentation, and retrieval. Finally, the book concludes with coverage the extension of fuzzy sets. This book: Covers both fuzzy and intuitionistic fuzzy sets and includes examples and practical applications Discusses intuitionistic fuzzy integrals and recent aggregation operators using Choquet integral, with examples Includes a chapter on applications in image processing using fuzzy and intuitionistic fuzzy sets Explains fuzzy matrix operations and features examples Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set is an ideal text for graduate and research students, as well as professionals, in image processing, decision-making, pattern recognition, and control system design.
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 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 Surfaces in GIS and Geographical Analysis: Theory, Analytical Methods, Algorithms and Applications
by Weldon LodwickSurfaces are a central to geographical analysis. Their generation and manipulation are a key component of geographical information systems (GISs). However, geographical surface data is often not precise. When surfaces are used to model geographical entities, the data inherently contains uncertainty in terms of both position and attribute. Fuzzy
Fuzzy System Identification and Adaptive Control (Communications and Control Engineering)
by Ruiyun Qi Gang Tao Bin JiangThis 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 Technology
by Janusz Kacprzyk Mario Fedrizzi Mikael CollanThisbook provides readers with a timely and comprehensive yet concise view on the field of fuzzy logic and its real-world applications. The chapters, writtenby authoritative scholars in the field, report on promising new models for dataanalysis, decision making, and systems modeling, with a special emphasis on theirapplications in management science. The book is a token of appreciation from the fuzzy researchcommunity to Professor Christer Carlsson for his long time research and organizationalcommitment, which have among other things resulted in the foundation and success of theInstitute for Advanced Management Systems Research (IAMSR) at Åbo Akademi University, in Åbo(Turku), Finland. The book serves as timely guide for the fuzzy logic andoperations research communities alike.
Fuzzy TOPSIS: Logic, Approaches, and Case Studies
by Mohamed El AlaouiThis book aims to justify the use of fuzzy logic as a logic and as a theory in the decision-making context. It also discusses the development of the TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) with related examples and MATLAB codes. This is the first book devoted to TOPSIS and its fuzzy versions. It presents the use of fuzzy logic as a logic and as a theory in the decision-making content and discusses the development of the TOPSIS method in classical and fuzzy context. The book justifies the use of fuzzy logic as an uncertainty theory and provides illustrative examples for each fuzzy TOPSIS extension, along with related MATLAB codes and case studies. This book is for Industrial Engineers, Operations Research Engineers, Systems Engineers, and Production Engineers working in the areas of Decision Analysis, Multi-Criteria Decision Making, and Multiple Objective Optimization.
Fuzzy Transportation and Transshipment Problems (Studies in Fuzziness and Soft Computing #385)
by Amarpreet Kaur Janusz Kacprzyk Amit KumarThis book presents a novel approach to the formulation and solution of three classes of problems: the fully fuzzy transportation problem, the fully fuzzy transshipment problem, and fully fuzzy solid transportation problem. It points out some limitations of the existing formulations and approaches, indicating some possible, conceptually and algorithmically attractive solutions to alleviate them. In particular, the book describes new conceptual and algorithmic solutions for finding the fuzzy optimal solutions of the single-objective fully fuzzy transportation problems, the fully fuzzy transshipment problems and the fully fuzzy solid transportation problems. Moreover, based on the novel concepts and solutions proposed by combining the concept of a fully fuzzy solid transportation problem and a fully fuzzy transshipment problem, it describes a new class of problems, i.e. the fully fuzzy solid trans-shipment problem, together with its fuzzy linear programming formulation and some methods to find its fuzzy optimal solution. The book offers the readers a timely piece of literature in the field of fuzzy linear programming, and is expected to act as a source of inspiration for future research and applications.
Fy Nodiadau Adolygu: CBAC TGA Bwyd a Maeth (My Revision Notes: WJEC GCSE Food and Nutrition Welsh-language edition)
by Helen BucklandExam Board: WJECLanguage: WelshLevel: GCSESubject: Food PreparationFirst Teaching: September 2016First Exam: Summer 2018Unlock your full potential with this revision guide that will guide you through the content and skills you need to succeed in the WJEC GCSE Food Preparation and Nutrition exam.- Plan your own revision and focus on the areas you need to revise with key fact summaries and revision activities for every topic.- Use the exam tips to clarify key points and avoid making typical mistakes.- Test yourself with end-of-topic questions and answers and tick off each topic as you complete it.- Get ready for the exam with tips on approaching the paper, and sample exam questions with model answers and commentary.