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• Recognising Handwritten Digits Using a Fuzzy Neural Network Joshua Reynolds and Tianhua Chen
• Fuzzy Assessment of Student Academic Performances Shangen Yang and Tianhua Chen
• A Hybrid Fuzzy Neural Network for Image Recognition Samaresh Nayak and Tianhua Chen
• A Fuzzy Diagnostic System for Heart Disease Siyue Song, Tianhua Chen, and Grigoris Antoniou
• Analysing Medical Notes using Fuzzy Logic Siyue Song, Tianhua Chen, and Grigoris Antoniou
• Fostering Positive Personalisation through Fuzzy Clustering Raymond Moodley
• Fuzzy Logic in Modern Information Retrieval Steve Wade
• Fuzzy Applied to Sentiment Analysis Orestes Appel
• Fuzzy Logic, a Logicians Perspective Patrick Fogarty
• Applications of Fuzzy Logic in an Automated Warehouse Patrick Fogarty
• Can Fuzzy Systems Assist with Project Planning? Daniel Maia and Arjab Khuman
• Fuzzy Logic in Autonomous Vehicles David McDougall and Arjab Khuman
• AI Spawning Fuzzy Logic Fuzzy Inference System Reece Carey and Arjab Khuman
• The Application of Fuzzy Logic on Intelligent Transportation Systems Nath Lloyd and Arjab Khuman
• Fuzzy Logic Applied to Water Processes Will Chapman and Arjab Khuman
• Applications of Fuzzy Logic in Autonomous Vehicles Sam Asquith and Arjab Khuman
• Predicting Cyber Threats using Fuzzy Logic Jarrad Morden and Arjab Khuman
• Implementations of Fuzzy Logic in Camera Systems Sophie Hughes and Arjab Khuman
• Application of a Fuzzy Logic Control System for Stock Market Prediction Based on Technical Indicators and Fundamental Analysis Humza Nazir and Arjab Khuman
• The Application of Fuzzy Logic in Determining Outcomes of Sporting Events Spencer Deane and Arjab Khuman
• Using Fuzzy Logic to Educate People on Phishing Harry Taylor and Arjab Khuman.
• (source: Nielsen Book Data)

Since 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.
(source: Nielsen Book Data)

• Part I: Type-1 and Type-2 Fuzzy Logic
• Parameter adaptation in the imperialist competitive algorithm using generalized type-2 fuzzy logic
• A Mamdani fuzzy inference protocol aimed to identify heterogeneity of the allometric response in arithmetical space
• Fireworks Algorithm (FWA) with adaptation of parameters using Interval Type-2 Fuzzy Logic System
• Omnidirectional Four Wheel Mobile Robot Control with a Type-2 Fuzzy Logic Behavior-based Strategy.

This book describes the latest advances in fuzzy logic, neural networks, and optimization algorithms, as well as their hybrid intelligent combinations, and their applications in the areas such as intelligent control, robotics, pattern recognition, medical diagnosis, time series prediction, and optimization. The topic is highly relevant as most current intelligent systems and devices use some form of intelligent feature to enhance their performance. The book also presents new and advanced models and algorithms of type-2 fuzzy logic and intuitionistic fuzzy systems, which are of great interest to researchers in these areas. Further, it proposes novel, nature-inspired optimization algorithms and innovative neural models. Featuring contributions on theoretical aspects as well as applications, the book appeals to a wide audience.

• List of Figures.- List of Tables.-
• Chapter 1. Introduction.-
• Chapter 2. Background.-
• Chapter 3. Methodology.-
• Chapter 4. Fuzzy Inference System, etc.
• (source: Nielsen Book Data)

This book provides a real-time and knowledge-based fuzzy logic model for soft tissue deformation. The demand for surgical simulation continues to grow, as there is a major bottleneck in surgical simulation designation and every patient is unique. Deformable models, the core of surgical simulation, play a crucial role in surgical simulation designation. Accordingly, this book (1) presents an improved mass spring model to simulate soft tissue deformation for surgery simulation; (2) ensures the accuracy of simulation by redesigning the underlying Mass Spring Model (MSM) for liver deformation, using three different fuzzy knowledge-based approaches to determine the parameters of the MSM; (3) demonstrates how data in Central Processing Unit (CPU) memory can be structured to allow coalescing according to a set of Graphical Processing Unit (GPU)-dependent alignment rules; and (4) implements heterogeneous parallel programming for the distribution of grid threats for Computer Unified Device Architecture (CUDA)-based GPU computing. .
(source: Nielsen Book Data)

• Elements of Intuitionistic Fuzzy Propositional Calculus.- Intuitionistic Fuzzy Predicate Logic.- Intuitionistic Fuzzy Modal Logics.- Temporal and Multidimensional Intuitionistic Fuzzy Logics.- Conclusion.
• (source: Nielsen Book Data)

The book offers a comprehensive survey of intuitionistic fuzzy logics. By reporting on both the author's research and others' findings, it provides readers with a complete overview of the field and highlights key issues and open problems, thus suggesting new research directions. Starting with an introduction to the basic elements of intuitionistic fuzzy propositional calculus, it then provides a guide to the use of intuitionistic fuzzy operators and quantifiers, and lastly presents state-of-the-art applications of intuitionistic fuzzy sets. The book is a valuable reference resource for graduate students and researchers alike.
(source: Nielsen Book Data)

• Introduction.- Meaning as a Quantity.- Antonyms, Negation, and the Fuzzy Case.- 'And', and 'Or' in Language. The case with Fuzzy Sets.- A First Look at Conditional Statements.- Linguistic Qualification, and Synonymy.- Thinking, Analogy, and Reasoning.- A (Naive) Symbolic Model of Ordinary Reasoning.- A Glance at Analogy.- A Glance at Creative Reasoning.- Formal Reasoning with Precise Words.- Formal Reasoning with Imprecise Words.- A Few Questions on the Reasoning of Quantum Physics.- Questions on Uncertain, Possible, and Probable.- Questions on Domesticating and Controlling Analogy.- Questions on the Classical Schemes of Inference.- Questions on the Fuzzy Schemes of Inference.- Questions on Monotony.- Questions on 'Not Covered by P'.- Questions on 'Sorites' in Ordinary Reasoning.- A Few Questions on Naming Concepts.- Instead of a Conclusion.- To End Up.
• (source: Nielsen Book Data)

This book offers an inspiring and naive view on language and reasoning. It presents a new approach to ordinary reasoning that follows the author's former work on fuzzy logic. Starting from a pragmatic scientific view on meaning as a quantity, and the common sense reasoning from a primitive notion of inference, which is shared by both laypeople and experts, the book shows how this can evolve, through the addition of more and more suppositions, into various formal and specialized modes of precise, imprecise, and approximate reasoning. The logos are intended here as a synonym for rationality, which is usually shown by the processes of questioning, guessing, telling, and computing. Written in a discursive style and without too many technicalities, the book presents a number of reflections on the study of reasoning, together with a new perspective on fuzzy logic and Zadeh's "computing with words" grounded in both language and reasoning. It also highlights some mathematical developments supporting this view. Lastly, it addresses a series of questions aimed at fostering new discussions and future research into this topic. All in all, this book represents an inspiring read for professors and researchers in computer science, and fuzzy logic in particular, as well as for psychologists, linguists and philosophers.
(source: Nielsen Book Data)

Real world problems are complex and full of uncertainty. Fuzzy computing technologies embedded into data analysis methodologies and decision-making processes help to address the complexity of these problems. Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization presents the most innovative systematic and practical facets of fuzzy computing technologies to students, scholars, and academicians, as well as practitioners, engineers, and professionals. This premier reference source focuses on up-to-date theoretical views of fuzzy computing while highlighting empirical approaches useful to real world utilization.
(source: Nielsen Book Data)

• Why Fuzzy Logic? - A Spectrum of Theoretical and Pragmatics Issues.- On Fuzzy Set Theories.- Uninorm Basics.- Structural Interpolation and Approximation with Fuzzy Relations: A Study in Knowledge Reuse.- On Fuzzy Logic and Chaos Theory.- Upper and Lower Values for the Level of Fuzziness in FCM.- Mathematical Modeling of Natural Phenomena: A Fuzzy Logic Approach.- Mathematical Fuzzy Logic in Modeling of Natural Language Semantics.- Analytical Theory of Fuzzy IF-THEN Rules with Compositional Rule of Inference.- Fuzzy Logic and Ontology-based Information Retrieval.- Real-World Fuzzy Logic Applications in Data Mining and Information Retrieval.- Gene Regulatory Network Modeling: A Data Driven Approach.- An Abstract Approach Toward the Evaluation of Fuzzy Rule Systems.- Nuclear Reactor Power Control Using State Feedback with Fuzzy Logic.- The Fusion of Genetic Algorithms and Fuzzy Classification for Transient Identification.- The Role of Fuzziness in Decision Making.- Fuzzy Linear Bilevel Optimization: Solution Concepts, Approaches and Applications.- Fuzzy Predictive Earth Analysis Constrained by Heuristics Applied to Stratigraphic Modeling.- Fuzzy Logic for Modeling the Management of Technology.
• (source: Nielsen Book Data)

How far can you take fuzzy logic, the brilliant conceptual framework made famous by George Klir? With this book, you can find out. The authors of this updated edition have extended Klir's work by taking fuzzy logic into even more areas of application. It serves a number of functions, from an introductory text on the concept of fuzzy logic to a treatment of cutting-edge research problems suitable for a fully paid-up member of the fuzzy logic community.
(source: Nielsen Book Data)

• Preface.
• 1. Abstract logic in a lattice.
• 2. Abstract fuzzy logic.
• 3. Extending an abstract crisp logic.
• 4. Approximate reasoning.
• 5. Logic as management of constraints on the truth values.
• 6. Canonical extension of a crisp Hilbert logic.
• 7. Graded consequence relations.
• 8. Truth-functional logic and fuzzy logic.
• 9. Probabilistic fuzzy logics.
• 10. Fuzzy control and approximate reasoning.
• 11. Effectiveness in fuzzy logic. References. Index. List of symbols.
• (source: Nielsen Book Data)

The theme of this book is fuzzy logic in a narrow sense, a promising new chapter of fuzzy logic. The basic ideas of formal logic were formulated by Lotfi Zadeh in 1975. The aim of this logic is to investigate the wonderful human capacity of reasoning with vague notions by attempting to formalize the 'approximate reasoning' we use in everyday life. A peculiarity of this book is to propose a general framework based on three mathematical tools: the theory of fuzzy closure operators, an extension principle for crisp logics and the theory of recursively enumerable fuzzy subsets.This book is unique in that it treats fuzzy logics which are not truth-functional in nature (as an example, the logic of the necessities, probabilistic logics and similarity-based logics). The book is addressed to people interested in artificial intelligence, fuzzy control, formal logic, and philosophy. It can be used in special post-graduate university studies and in advanced courses. The book is completely self-contained.
(source: Nielsen Book Data)

• Foreword by Lotfi Zadeh Preface INTRODUCTION Background. Why fuzzy logic? FUNDAMENTAL CONCEPTS OF FUZZY COMPUTATION Vagueness and uncertainty: Theories of vagueness. Theories of uncertainty. Fuzzy sets: concepts and characterizations: Introduction. Operations on fuzzy sets. Interpretations of fuzzy sets. Fuzzy relations. Characterization of fuzzy sets. Fuzzy measure and integral. Fuzzy mathematical objects. Extension principle. Fuzzy set calculus: Introduction. Membership function elicitation. Fuzzy relational calculus. Fuzzy arithmetic. Possibility theory. Fuzzy reasoning: Introduction. Fuzzy inference. Defuzzification. FUZZY MODELS Fuzzy models. Modeling and simulation: Granule-based models. Logical aspects of fuzzy models. Statistical models. Fuzzy Petri Net model. Model acquisition. Approximation aspects of fuzzy models. HYBRID APPROACHES Introduction: motivation for hybrid approaches. Neuro-fuzzy systems. Fuzzy-evolutionary systems. FUZZY COMPUTATION ENVIRONMENTS Software approaches: Programming languages. Knowledge-based systems. Database management, information retrieval, and decision support systems. Hardware approaches: Desirable features. Adapting existing hardware to fuzzy computation. Analog approaches. Digital approaches. Hybrid (digital-analog) approaches. APPLICATIONS OF FUZZY COMPUTATION Knowledge based systems: Knowledge representation. Inference methods. Control methods. Design methods. Control. Principles of fuzzy controllers. Fuzzy control approaches: General design schemes. Cell maps. Sliding mode control. Predictive control. Hierarchical control. Model-based control. Optimal fuzzy control. Machine learning: Introduction: learning fuzzy concepts. Supervised learning. Reinforcement learning. Data and information management: Fuzzy databases. Information retrieval. Case-based reasoning. Decision making and optimization: Decision-making models. Optimization. Pattern analysis. Computer vision. FUZZY COMPUTATION IN PRACTICE Aerospace: Proximity operations spacecraft controller: a case study in fuzzy logic control. Systems control: DC/DC converters fuzzy control. Fuzzy control in telecommunications. Fuzzy-neural traffic control and forecasting. Systems control. Backlash compensation using fuzzy logic. Neurofuzzy modeling for nonlinear system identification. Nuclear engineering: Application of fuzzy logic control system for nuclear reactor control. Manufacturing: Applications of fuzzy set methodologies in manufacturing. Compensation of friction in mechanical positioning systems. Diagnostics: Possibilistic handling of uncertainty in fault diagnosis. Robotics: Autonomous mobile robot control. Chemical engineering: Chemical engineering application. Water treatment: Water treatment application. Automotive: Improvement of the relationship between driver and vehicle using fuzzy logic. Traffic engineering: Traffic engineering application. Civil engineering: Civil engineering application. Engineering design: A fuzzy sets application to preliminary passenger vehicle structure design. Oil refining: Neuro-fuzzy hybrid control system in petroleum plant. Medicine: CADIA
• G2: hospital-based computer-assisted differential diagnosis in internal medicine. Neural networks for ECG diagnostic classification. Information science: Case-based reasoning. Information retrieval: a case study of the CASHE: PVS systems. Economics, finance and business. Decision support system for foreign exchange trade (FOREX). Operations research: Scheduling. Fuzzy sets in operation research: forecasting, a case study. Quality design using possibilistic regression and optimization. Inventory control. Time series prediction. FUZZY COMPUTATION RESEARCH Directions for future research. APPENDICES.
• (source: Nielsen Book Data)

Initially conceived as a methodology for the representation and manipulation of imprecise and vague information, fuzzy computation has found wide use in problems that fall well beyond its originally intended scope of application. Many scientists and engineers now use the paradigms of fuzzy computation to tackle problems that are either intractable or unrealistically time consuming. The extraordinary growth of fuzzy computation in recent years has led to the development of numerous systems of major practical importance in fields ranging from medical diagnosis and automated learning to image understanding and systems control."The Handbook of Fuzzy Computation" provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. Divided into several parts, the handbook presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, and system control and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. By focusing on the application of fuzzy computation methods to solve problems, this all-encompassing reference work enables scientists and engineers to understand the central aspects of this important computational methodology.
(source: Nielsen Book Data)

• Foreword by Lotfi Zadeh Preface INTRODUCTION Background. Why fuzzy logic? FUNDAMENTAL CONCEPTS OF FUZZY COMPUTATION Vagueness and uncertainty: Theories of vagueness. Theories of uncertainty. Fuzzy sets: concepts and characterizations: Introduction. Operations on fuzzy sets. Interpretations of fuzzy sets. Fuzzy relations. Characterization of fuzzy sets. Fuzzy measure and integral. Fuzzy mathematical objects. Extension principle. Fuzzy set calculus: Introduction. Membership function elicitation. Fuzzy relational calculus. Fuzzy arithmetic. Possibility theory. Fuzzy reasoning: Introduction. Fuzzy inference. Defuzzification. FUZZY MODELS Fuzzy models. Modeling and simulation: Granule-based models. Logical aspects of fuzzy models. Statistical models. Fuzzy Petri Net model. Model acquisition. Approximation aspects of fuzzy models. HYBRID APPROACHES Introduction: motivation for hybrid approaches. Neuro-fuzzy systems. Fuzzy-evolutionary systems. FUZZY COMPUTATION ENVIRONMENTS Software approaches: Programming languages. Knowledge-based systems. Database management, information retrieval, and decision support systems. Hardware approaches: Desirable features. Adapting existing hardware to fuzzy computation. Analog approaches. Digital approaches. Hybrid (digital-analog) approaches. APPLICATIONS OF FUZZY COMPUTATION Knowledge based systems: Knowledge representation. Inference methods. Control methods. Design methods. Control. Principles of fuzzy controllers. Fuzzy control approaches: General design schemes. Cell maps. Sliding mode control. Predictive control. Hierarchical control. Model-based control. Optimal fuzzy control. Machine learning: Introduction: learning fuzzy concepts. Supervised learning. Reinforcement learning. Data and information management: Fuzzy databases. Information retrieval. Case-based reasoning. Decision making and optimization: Decision-making models. Optimization. Pattern analysis. Computer vision. FUZZY COMPUTATION IN PRACTICE Aerospace: Proximity operations spacecraft controller: a case study in fuzzy logic control. Systems control: DC/DC converters fuzzy control. Fuzzy control in telecommunications. Fuzzy-neural traffic control and forecasting. Systems control. Backlash compensation using fuzzy logic. Neurofuzzy modeling for nonlinear system identification. Nuclear engineering: Application of fuzzy logic control system for nuclear reactor control. Manufacturing: Applications of fuzzy set methodologies in manufacturing. Compensation of friction in mechanical positioning systems. Diagnostics: Possibilistic handling of uncertainty in fault diagnosis. Robotics: Autonomous mobile robot control. Chemical engineering: Chemical engineering application. Water treatment: Water treatment application. Automotive: Improvement of the relationship between driver and vehicle using fuzzy logic. Traffic engineering: Traffic engineering application. Civil engineering: Civil engineering application. Engineering design: A fuzzy sets application to preliminary passenger vehicle structure design. Oil refining: Neuro-fuzzy hybrid control system in petroleum plant. Medicine: CADIA
• G2: hospital-based computer-assisted differential diagnosis in internal medicine. Neural networks for ECG diagnostic classification. Information science: Case-based reasoning. Information retrieval: a case study of the CASHE: PVS systems. Economics, finance and business. Decision support system for foreign exchange trade (FOREX). Operations research: Scheduling. Fuzzy sets in operation research: forecasting, a case study. Quality design using possibilistic regression and optimization. Inventory control. Time series prediction. FUZZY COMPUTATION RESEARCH Directions for future research. APPENDICES.
• (source: Nielsen Book Data)

Initially conceived as a methodology for the representation and manipulation of imprecise and vague information, fuzzy computation has found wide use in problems that fall well beyond its originally intended scope of application. Many scientists and engineers now use the paradigms of fuzzy computation to tackle problems that are either intractable or unrealistically time consuming. The extraordinary growth of fuzzy computation in recent years has led to the development of numerous systems of major practical importance in fields ranging from medical diagnosis and automated learning to image understanding and systems control."The Handbook of Fuzzy Computation" provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. Divided into several parts, the handbook presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, and system control and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. By focusing on the application of fuzzy computation methods to solve problems, this all-encompassing reference work enables scientists and engineers to understand the central aspects of this important computational methodology.
(source: Nielsen Book Data)

• One / Preliminaries
• 1.1 Introduction
• 1.2 A survey of Boolean propositional logic
• 1.3 Boolean predicate calculus
• 1.4 Function symbols; varieties of algebras
• 1.5 Lattices and Boolean algebras
• 1.6 Ordered Abelian groups
• Two / Many-valued propositional calculi
• 2.1 Continuous t-norms and their residua
• 2.2 The basic many-valued logic
• 2.3 Residuated lattices; a completeness theorem
• 2.4 Some additional topics
• Three /?ukasiewicz propositional logic
• 3.1 Getting?ukasiewicz logic
• 3.2 MV-algebras; a completeness theorem
• 3.3 Rational Pavelka logic
• Four / Product logic, G?del logic
• 4.1 Product logic
• 4.2 G?del logic
• 4.3 Appendix: Boolean logic
• Five / Many-valued predicate logics
• 5.1 The basic many-valued predicate logic
• 5.2 Completeness
• 5.3 Axiomatizing G?del logic
• 5.4?ukasiewicz and product predicate logic
• 5.5 Many-sorted fuzzy predicate calculi
• 5.6 Similarity and equality
• Six / Complexity and undecidability
• 6.1 Preliminaries
• 6.2 Complexity of fuzzy propositional calculi
• 6.3 Undecidability of fuzzy logics
• Seven / On approximate inference
• 7.1 The compositional rule of inference
• 7.2 Fuzzy functions and fuzzy controllers
• 7.3 An alternative approach to fuzzy rules
• Eight / Generalized quantifiers and modalities
• 8.1 Generalized quantifiers in Boolean logic
• 8.2 Two-valued modal logics
• 8.3 Fuzzy quantifiers and modalities
• 8.4 On?probably? and?many?
• 8.5 More on?probably? and?many?
• Nine / Miscellanea
• 9.1 Takeuti-Titani fuzzy logic
• 9.2 An abstract fuzzy logic
• 9.3 On the liar paradox
• 9.4 Concluding remarks
• Ten / Historical remarks
• 10.1 Until the forties
• 10.2 The fifties
• 10.3 The sixties
• 10.4 The seventies
• 10.5 The eighties
• 10.6 The nineties
• References.

This book presents a systematic treatment of deductive aspects and structures of fuzzy logic understood as many valued logic sui generis. Some important systems of real-valued propositional and predicate calculus are defined and investigated. The aim is to show that fuzzy logic as a logic of imprecise (vague) propositions does have well-developed formal foundations and that most things usually named `fuzzy inference' can be naturally understood as logical deduction.

• Preliminaries
• many-valued propositional calculi
• Lukasiewicz propositional logic
• product logic, Godel logic
• many-valued predicate logics
• complexity and undecidability
• on approximate inference
• generalized quantifiers and modalities
• miscellanea
• historical remarks.
• (source: Nielsen Book Data)

This book presents a systematic treatment of deductive aspects and structures of fuzzy logic understood as many valued logic sui generis. Some important systems of real-valued propositional and predicate calculus are defined and investigated. The aim is to show that fuzzy logic as a logic of imprecise (vague) propositions does have well-developed formal foundations and that most things usually named 'fuzzy inference' can be naturally understood as logical deduction. There are two main groups of intended readers. First, logicians: they can see that fuzzy logic is indeed a branch of logic and may find several very interesting open problems. Second, equally important, researchers involved in fuzzy logic applications and soft computing. As a matter of fact, most of these are not professional logicians so that it can easily happen that an application, clever and successful as it may be, is presented in a way which is logically not entirely correct or may appear simple-minded.(Standard presentations of the logical aspects of fuzzy controllers are the most typical example.) This fact would not be very important if only the bon ton of logicians were harmed; but it is the opinion of the author (who is a mathematical logician) that a better understanding of the strictly logical basis of fuzzy logic (in the usual broad sense) is very useful for fuzzy logic appliers since if they know better what they are doing, they may hope to do it better. In addition, a better mutual understanding between (classical) logicians and researchers in fuzzy logic, promises to lead to deeper cooperation and new results. Intended audience: mathematicians, logicians, computer scientists, specialists in artificial intelligence and knowledge engineering, developers of fuzzy logic.
(source: Nielsen Book Data)

Fuzzy logic has become an important tool for a number of different applications ranging from the control of engineering systems to artificial intelligence. In this concise introduction, the author presents a succinct guide to the basic ideas of fuzzy logic, fuzzy sets, fuzzy relations, and fuzzy reasoning, and shows how they may be applied. The book culminates in a chapter which describes fuzzy logic control: the design of intelligent control systems using fuzzy if-then rules which make use of human knowledge and experience to behave in a manner similar to a human controller. Throughout, the level of mathematical knowledge required is kept basic and the concepts are illustrated with numerous diagrams to aid in comprehension. As a result, all those curious to know more about fuzzy concepts and their real-world application will find this a good place to start.
(source: Nielsen Book Data)

• Fuzzy Logic: A Framework for the New Millennium
• 1. Understanding Society
• to Fuzziology
• Why Does Fuzzy Logic Need the Challenge of Social Complexity?
• Fuzziology and Lifelong Learning
• Use of Fuzziology when Dealing with Hard Negotiation
• Fuzzy and Amorphic Boundaries in Intercultural Space: The Case of Mexico and the USA
• Self-Organisation of the Community: Democratic Republic or Anarchic Utopia
• The Poetic License of the Fuzzy Paradigm
• Fuzzy Interplay of Contrasts as Perception-Based Invariant in Art and Design
• Human Behaviour: Moving From Dizzy to Fuzzy
• Fuzziology in Search of Insights for Understanding Global Economy
• 2. Mathematics and Modelling
• On Self-Organization of Cooperative Systems and Fuzzy Logic
• Evolutionary Optimization of Fuzzy Models
• Sugeno Integral from a Point of View of Fuzzy Logic
• On Understanding the Structure of Variance-Covariance Matrix for Dealing with Fuzziness in Financial Markets
• Towards Robot Soccer Controlled by Fuzzy Logic
• Indexing and Retrieval of Fuzzy Shapes
• 3. Control Systems
• Reasoning and Analysis of Uncertainties within Command and Control Navigation Simulation Using Fuzzy Logic Petri Net
• Indirect Adaptive Fuzzy Control of Nonlinear Systems with Terminal Sliding Modes
• A New Approach in Fuzzy Adaptive Filtering
• Exploring the Explanatory Capabilities of Intelligent System Technologies
• Integrating Soft Computing Classification Technique for Linguistic Data in a Petroleum Engineering Application
• P-Expert: A Prototype Expert Advisory System in the Management and Control of Parthenium Weed in Central Queensland
• Designing a Fuzzy Gain Lyapunov Adaptive Filter Algorithm
• Neuro-Fuzzy Control Applications: Looking for New Areas and Techniques?
• Modelling Coral Bleaching Events Using a Fuzzy Logic Technique
• Sources, Measurements and Models of Type 2 Fuzziness in the New Millennium
• Learning Ecology for Human and Machine Intelligence: A Soft Computing Approach.

The book focuses on both the engineering applications of fuzzy logic and soft computing and its social applications and philosophical insights at the dawn of the third millennium. The included papers clearly demonstrate that fuzzy logic revolutionizes general approaches to solving applied problems and reveals deep connections between those approaches and the underlying unique theoretical framework. The book consists of three parts: Understanding Society, Mathematics, and Modelling and Control Systems. The first part reveals different aspects of fuzziology, a new study of fuzziness inherent in human knowledge. The second part explores the mathematical foundations of soft computing, while the third part elicits its innovative engineering applications.