Witold Pedrycz - Böcker

Soft Numerical Computing in Uncertain Dynamic Systems is intended for system specialists interested in dynamic systems that operate at different time scales. The book discusses several types of errors and their propagation, covering numerical methods-including convergence and consistence properties and characteristics-and proving of related theorems within the setting of soft computing. Several types of uncertainty representation like interval, fuzzy, type 2 fuzzy, granular, and combined uncertain sets are discussed in detail. The book can be used by engineering students in control and finite element fields, as well as all engineering, applied mathematics, economics, and computer science students.

One of the important topics in applied science is dynamic systems and their applications. The authors develop these models and deliver solutions with the aid of numerical methods. Since they are inherently uncertain, soft computations are of high relevance here. This is the reason behind investigating soft numerical computing in dynamic systems. If these systems are involved with complex-uncertain data, they will be more practical and important. Real-life problems work with this type of data and most of them cannot be solved exactly and easily-sometimes they are impossible to solve.

Clearly, all the numerical methods need to consider error of approximation. Other important applied topics involving uncertain dynamic systems include image processing and pattern recognition, which can benefit from uncertain dynamic systems as well. In fact, the main objective is to determine the coefficients of a matrix that acts as the frame in the image. One of the effective methods exhibiting high accuracy is to use finite differences to fill the cells of the matrix.

Explores dynamic models, how time is fundamental to the structure of the model and data, and how a process unfolds Investigates the dynamic relationships between multiple components of a system in modeling using mathematical models and the concept of stability in uncertain environments Exposes readers to many soft numerical methods to simulate the solution function's behavior

New paradigms for communication/networking systems are needed in order to tackle the emerging issues such as heterogeneity, complexity and management of evolvable infrastructures. Such advanced systems possess a composition of characteristics including: Situated: they sense and react locally onto environment and context changes. Autonomously managed: their actions are decided using local knowledge and self-managing principles. Colloborative: they communicate and share knowledge optimizing functionality for suvivability. The intelligence of such networks relies not on the nodes themselves, but is directly associated with their size, complexity, communication and collaboration capabilities. Self-organizing: they reorganize and form communities on-the-fly based on the local/global goals and communication needs. Highly distributed: they use decentralized optimization strategies and cross-layer or non-layered approaches should be applicable. Technology independent: they will be built on abstraction layers that make it possible to develop modular approaches where software and hardware components can be exchanged on the fly.Scale-free: they can scale up to very large numbers without exhibiting any visible impact on the performance and functionality.In order to realize such advanced systems, communcation/networking approaches should become task-and knowledge-driven, enabling a service oriented, requirement and trust driven development of communication networks. This should allow developing networking technologies (hardware/software combinations) that can evolve and create maximal synergy with the other types of non-technological networks that consitute their context. The growing complexity of control requires increasingly distributed and self-organizing structures, relying on simple and dependable elements that are able to collaborate to produce sophisticated behaviors and that can adapt to an evolving situation, in which new resources can become available, administrative domains can change and economic models can vary accordingly.The networking and seamless integration of concepts, technologies, devies in a dynamically changing environment sets many many challenges to the research community, including interoperability, programmability, management, openness, reliability, performance, context awareness, intelligence, autonomy, security, privacy, safety, semantics, etc.This edited volume explores the challenges in technolgies that will help realize the vision where devices and applications seamlessly interconnect, intelligently cooperate and autonomously manage themselves, and as a result, the borders of virtual and real world will vanish or become significantly blurred.

“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems.Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.Introduces mathematics of intelligent systems which provides the usage of mathematical rigor such as precise definitions, theorems, results, and proofsProvides extended and new comprehensive methods which can be used efficiently in a fuzzy environment as well as optimization problems and related fieldsCovers applications and elaborates on the usage of the developed methodology in various fields of industry such as software technologies, biomedicine, image processing, and communications

Emerging Fuzzy Intelligent Systems for Smart Healthcare Management: Applications of Disc q-Rung Orthopair Fuzzy Sets presents comprehensive methodological frameworks and the latest empirical research findings concerning disc q-rung orthopair fuzzy operators, with a specific focus on their applications in smart technologies for healthcare management. The book solves a crucial problem by offering readers an invaluable opportunity to conduct a comparative analysis, contrasting the proposed methods with their existing knowledge base. Disc q-Rung Orthopair sets, being the generalization of q-Rung Orthopair fuzzy sets, which are, in turn, the generalization of Pythagorean fuzzy sets, extend the capabilities of handling uncertainty beyond conventional fuzzy sets.The authors strive to narrow the knowledge gap by clarifying the practical applications of disc q-rung orthopair fuzzy logic. In addition, it explores an enhanced version of q-Rung Orthopair Fuzzy Sets, specifically focusing on Disc q-Rung Orthopair Fuzzy Sets, introducing various types of operators. These operators play a crucial role in solving decision-making and optimization problems. A notable contribution is the development of a hybrid operator, termed as the Disc q-Rung Orthopair Fuzzy Hybrid Weighted Averaging/Geometric (D-qROFHWA/G) operator.Proposes a novel enhancement in the realm of q-rung orthopair fuzzy operatorsProvides insights for both practitioners and academia, focusing on debated aspects in the fieldCovers Advanced Aggregation Operators and MADM Methods for Smart Healthcare ManagementDiscusses comparative analysis and integration with established mathematical results

Collaborative Scheduling for Electromagnetic Detection Satellites: Model and Reinforcement Learning-based Evolutionary Algorithms focuses on the models and algorithms related to electromagnetic detection satellite scheduling problems. Sections provide an overview, including background and current state of development and then move on to the basic model of cooperative scheduling and the framework of learning evolutionary algorithms. Next, the book examines cooperative scheduling of homogeneous electromagnetic detection satellites for stationary target detection and reviews research on cooperative planning of heterogeneous electromagnetic detection satellites for low-speed moving target detection. Final sections examine cooperative scheduling of heterogeneous electromagnetic detection satellites for high-speed moving target detection. Readers will find this to be an in-depth introduction to electromagnetic detection satellite problems.Highlights Collaborative Scheduling for Electromagnetic Detection SatellitesProvides the first book to address the problem of scheduling for electromagnetic detection satellitesPresents a new model of construction and algorithmsContains a large volume of pseudo-code for readers to useIncludes simulation experiments that can be used by researchers for comparative reference

A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering. Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book addresses timely and relevant concepts and methods, whilst identifying major challenges and recent developments in the area. Split into five clear sections, Fundamentals, Visualization, Algorithms and Computational Aspects, Real-Time and Dynamic Clustering, and Applications and Case Studies, the book covers a wealth of novel, original and fully updated material, and in particular offers: a focus on the algorithmic and computational augmentations of fuzzy clustering and its effectiveness in handling high dimensional problems, distributed problem solving and uncertainty management.presentations of the important and relevant phases of cluster design, including the role of information granules, fuzzy sets in the realization of human-centricity facet of data analysis, as well as system modellingdemonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspectsa carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal roleThis book will be of key interest to engineers associated with fuzzy control, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering disciplines, will find this an invaluable resource and research tool.

Although the notion is a relatively recent one, the notions and principles of Granular Computing (GrC) have appeared in a different guise in many related fields including granularity in Artificial Intelligence, interval computing, cluster analysis, quotient space theory and many others. Recent years have witnessed a renewed and expanding interest in the topic as it begins to play a key role in bioinformatics, e-commerce, machine learning, security, data mining and wireless mobile computing when it comes to the issues of effectiveness, robustness and uncertainty. The Handbook of Granular Computing offers a comprehensive reference source for the granular computing community, edited by and with contributions from leading experts in the field.   Includes chapters covering the foundations of granular computing, interval analysis and fuzzy set theory; hybrid methods and models of granular computing; and applications and case studies.Divided into 5 sections: Preliminaries, Fundamentals, Methodology and Algorithms, Development of Hybrid Models and Applications and Case Studies.Presents the flow of ideas in a systematic, well-organized manner, starting with the concepts and motivation and proceeding to detailed design that materializes in specific algorithms, applications and case studies.Provides the reader with a self-contained reference that includes all pre-requisite knowledge, augmented with step-by-step explanations of more advanced concepts.The Handbook of Granular Computing represents a significant and valuable contribution to the literature and will appeal to a broad audience including researchers, students and practitioners in the fields of Computational Intelligence, pattern recognition, fuzzy sets and neural networks, system modelling, operations research and bioinformatics.

Fuzzy 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.

Peters and Pedrycz have written the first comprehensive and complete text on a quantitative approach to Software Engineering. This book provides the reader with well-defined and carefully described software practices based on industry standards. It presents practical approaches to specifying, designing and testing software as well as the foundations of Software Engineering. Frameworks, methods and technologies in aid of the activities typically found in software projects, are thoroughly presented. This book includes a complete case study representing all of the major phases in software development.Emphasizing both the design and analysis of the technology, Peters and Pedrycz have created a highly balanced introduction to Software Engineering. This text provides students with a holistic look at software design by encouraging them to view the process as an interplay between hardware and software. The reader will also find the latest information in the field, frequent references to related web sites, a glossary of technical terms and acronyms, and supplementary material at the authors' web site.This text is suitable for a one semester, junior/senior undergraduate course in Software Engineering.

A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topicsCovers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangibleIncludes illustrative material andwell-known experimentsto offer hands-on experience

A self-contained treatment of fuzzy systems engineering, offering conceptual fundamentals, design methodologies, development guidelines, and carefully selected illustrative material Forty years have passed since the birth of fuzzy sets, in which time a wealth of theoretical developments, conceptual pursuits, algorithmic environments, and other applications have emerged. Now, this reader-friendly book presents an up-to-date approach to fuzzy systems engineering, covering concepts, design methodologies, and algorithms coupled with interpretation, analysis, and underlying engineering knowledge. The result is a holistic view of fuzzy sets as a fundamental component of computational intelligence and human-centric systems. Throughout the book, the authors emphasize the direct applicability and limitations of the concepts being discussed, and historical and bibliographical notes are included in each chapter to help readers view the developments of fuzzy sets from a broader perspective. A radical departure from current books on the subject, Fuzzy Systems Engineering presents fuzzy sets as an enabling technology whose impact, contributions, and methodology stretch far beyond any specific discipline, making it applicable to researchers and practitioners in engineering, computer science, business, medicine, bioinformatics, and computational biology. Additionally, three appendices and classroom-ready electronic resources make it an ideal textbook for advanced undergraduate- and graduate-level courses in engineering and science.

It took many decades for Peirce's coneept of a relation to find its way into the microelectronic innards of control systems of eement kilns, subway trains, and tunnel-digging machinery. But what is amazing is that the more we leam about the basically simple coneept of a relation, the more aware we become of its fundamental importanee and wide ranging ramifications. The work by Di Nola, Pedrycz, Sanchez, and Sessa takes us a long distanee in this direction by opening new vistas on both the theory and applications of fuzzy relations - relations which serve to model the imprecise coneepts which pervade the real world. Di Nola, Pedrycz, Sanchez, and Sessa focus their attention on a eentral problem in the theory of fuzzy relations, namely the solution of fuzzy relational equations. The theory of such equations was initiated by Sanchez in 1976, ina seminal paper dealing with the resolution of composite fuzzy relational equations. Sinee then, hundreds of papers have been written on this and related topics, with major contributions originating in France, Italy, Spain, Germany, Poland, Japan, China, the Soviet Union, India, and other countries. The bibliography included in this volume highlights the widespread interest in the theory of fuzzy relational equations and the broad spectrum of its applications.

This volume provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. The book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. It then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Illustrative examples and experimental findings are also included.

This work provides a compendium of fuzzy models, identification algorithms and applications. Chapters have been written by leading scholars and researchers in their respective subject areas, and include both theoretical material and applications. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, and so forth, identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models.The book provides specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included are case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.

The main theme of this text is to highlight a synergistic effect that is emerging between fuzzy sets and evolutionary computation. This quantifies the main advantage arising from the symbiosis. The scope of the book is broad, ranging from coverage of fundamental ideas in fuzzy sets and evolutionary computation, through inclusion of cutting-edge research, to case studies. The focus is on the applied side of fuzzy evolutionary calculations. Each contribution is systematic and thorough in its presentations, and emphasizes design of evolutionary schemes that embraces various sources of domain knowledge. The authors have also included problem sets at the end of each chapter which explore specific conceptual and algorithmic points covered in the text.

Telecommunications has evolved and grown at an explosive rate in recent years and will undoubtedly continue to do so. As its functions, applications, and technology grow, it becomes increasingly complex and difficult, if not impossible, to meet the demands of a global network using conventional computing technologies. Computational intelligence (CI) is the technology of the future-and the future is now.Computational Intelligence in Telecommunications Networks offers an in-depth look at the rapid progress of CI technology and shows its importance in solving the crucial problems of future telecommunications networks. It covers a broad range of topics, from Call Admission Control, congestion control, and QoS-routing for ATM networks, to network design and management, optical, mobile, and active networks, and Intelligent Mobile Agents.Today's telecommunications professionals need a working knowledge of CI to exploit its potential to overcome emerging challenges. The CI community must become acquainted with those challenges to take advantage of the enormous opportunities the telecommunications field offers. This text meets both those needs, clearly, concisely, and with a depth certain to inspire further theoretical and practical advances.

The book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is self-contained, addressing concepts, methodology, algorithms, and case studies and applications, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts: Part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morphology and use in diagnosis; Part II deals with techniques and models of computational intelligence that are suitable for signal processing; and Part III details ECG system-diagnostic interpretation and knowledge acquisition architectures. Illustrative material includes: brief numerical experiments; detailed schemes, exercises and more advanced problems.

Numerical analysis forms a cornerstone of numeric computing and optimization, in particular recently, interval numerical computations play an important role in these topics. The interest of researchers in computations involving uncertain data, namely interval data opens new avenues in coping with real-world problems and deliver innovative and efficient solutions. This book provides the basic theoretical foundations of numerical methods, discusses key technique classes, explains improvements and improvements, and provides insights into recent developments and challenges.The theoretical parts of numerical methods, including the concept of interval approximation theory, are introduced and explained in detail. In general, the key features of the book include an up-to-date and focused treatise on error analysis in calculations, in particular the comprehensive and systematic treatment of error propagation mechanisms, considerations on the quality of data involved in numerical calculations, and a thorough discussion of interval approximation theory.Moreover, this book focuses on approximation theory and its development from the perspective of linear algebra, and new and regular representations of numerical integration and their solutions are enhanced by error analysis as well. The book is unique in the sense that its content and organization will cater to several audiences, in particular graduate students, researchers, and practitioners.

Numerical analysis forms a cornerstone of numeric computing and optimization, in particular recently, interval numerical computations play an important role in these topics. The interest of researchers in computations involving uncertain data, namely interval data opens new avenues in coping with real-world problems and deliver innovative and efficient solutions. This book provides the basic theoretical foundations of numerical methods, discusses key technique classes, explains improvements and improvements, and provides insights into recent developments and challenges.The theoretical parts of numerical methods, including the concept of interval approximation theory, are introduced and explained in detail. In general, the key features of the book include an up-to-date and focused treatise on error analysis in calculations, in particular the comprehensive and systematic treatment of error propagation mechanisms, considerations on the quality of data involved in numerical calculations, and a thorough discussion of interval approximation theory.Moreover, this book focuses on approximation theory and its development from the perspective of linear algebra, and new and regular representations of numerical integration and their solutions are enhanced by error analysis as well. The book is unique in the sense that its content and organization will cater to several audiences, in particular graduate students, researchers, and practitioners.

A new approach to the issue of data quality in pattern recognitionDetailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal.For decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been data—its sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. Pattern Recognition: A Quality of Data Perspective repositions that challenge from a hurdle to a given, and presents a new framework for comprehensive data analysis that is designed specifically to accommodate problem data.Designed as both a practical manual and a discussion about the most useful elements of pattern recognition innovation, this book: Details fundamental pattern recognition concepts, including feature space construction, classifiers, rejection, and evaluationProvides a systematic examination of the concepts, design methodology, and algorithms involved in pattern recognitionIncludes numerous experiments, detailed schemes, and more advanced problems that reinforce complex conceptsActs as a self-contained primer toward advanced solutions, with detailed background and step-by-step processesIntroduces the concept of granules and provides a framework for granular computingPattern recognition plays a pivotal role in data analysis and data mining, fields which are themselves being applied in an expanding sphere of utility. By facing the data quality issue head-on, this book provides students, practitioners, and researchers with a clear way forward amidst the ever-expanding data supply.

A guide to the various models and methods to multicriteria decision-making in conditions of uncertainty presented in a systematic approachMulticriteria Decision-Making under Conditions of Uncertainty presents approaches that help to answer the fundamental questions at the center of all decision-making problems: "What to do?" and "How to do it?" The book explores methods of representing and handling diverse manifestations of the uncertainty factor and a multicriteria nature of problems that can arise in system design, planning, operation, and control. The authors—noted experts on the topic—and their book covers essential questions, including notions and fundamental concepts of fuzzy sets, models and methods of multiobjective as well as multiattribute decision-making, the classical approach to dealing with uncertainty of information and its generalization for analyzing multicriteria problems in condition of uncertainty, and more.This comprehensive book contains information on "harmonious solutions" in multiobjective problem-solving (analyzing  models), construction and analysis of models, results aimed at generating robust solutions in analyzing multicriteria problems under uncertainty, and more. In addition, the book includes illustrative examples of various applications, including real-world case studies related to the authors’ various industrial projects. This important resource: Explains the design and processing aspect of fuzzy sets, including construction of membership functions, fuzzy numbers, fuzzy relations, aggregation operations, and fuzzy sets transformationsDescribes models of multiobjective decision-making ( models), their analysis on the basis of using the Bellman-Zadeh approach to decision-making in a fuzzy environment, and their diverse applications, including multicriteria allocation of resourcesInvestigates models of multiattribute decision-making ( models) and their analysis on the basis of the construction and processing of fuzzy preference relations as well as demonstrating their applications to solve diverse classes of multiattribute problemsExplores notions of payoff matrices and fuzzy-set-based generalization and modification of the classic approach to decision-making under conditions of uncertainty to generate robust solutions in analyzing multicriteria problems Written for students, researchers and practitioners in disciplines in which decision-making is of paramount relevance, Multicriteria Decision-Making under Conditions of Uncertainty presents a systematic and current approach that encompasses a range of models and methods as well as new applications.

Information granules, as encountered in natural language, are implicit in nature. To make them fully operational so they can be effectively used to analyze and design intelligent systems, information granules need to be made explicit. An emerging discipline, granular computing focuses on formalizing information granules and unifying them to create a coherent methodological and developmental environment for intelligent system design and analysis. Granular Computing: Analysis and Design of Intelligent Systems presents the unified principles of granular computing along with its comprehensive algorithmic framework and design practices. Introduces the concepts of information granules, information granularity, and granular computingPresents the key formalisms of information granulesBuilds on the concepts of information granules with discussion of higher-order and higher-type information granulesDiscusses the operational concept of information granulation and degranulation by highlighting the essence of this tandem and its quantification in terms of the associated reconstruction errorExamines the principle of justifiable granularityStresses the need to look at information granularity as an important design asset that helps construct more realistic models of real-world systems or facilitate collaborative pursuits of system modelingHighlights the concepts, architectures, and design algorithms of granular modelsExplores application domains where granular computing and granular models play a visible role, including pattern recognition, time series, and decision makingWritten by an internationally renowned authority in the field, this innovative book introduces readers to granular computing as a new paradigm for the analysis and synthesis of intelligent systems. It is a valuable resource for those engaged in research and practical developments in computer, electrical, industrial, manufacturing, and biomedical engineering. Building from fundamentals, the book is also suitable for readers from nontechnical disciplines where information granules assume a visible position.

Granular computing is an emerging conceptual and computing paradigm of information processing. It has been motivated by the urgent need for intelligent processing of empirical data that is now commonly available in vast quantities, into a humanly manageable abstract knowledge. In this sense, granular computing offers a landmark change from the current machine-centric to human-centric approach to information and knowledge. The theoretical foundations of granular computing are exceptionally sound and involve set theory (interval mathematics), fuzzy sets, rough sets, and random sets linked together in a highly comprehensive treatment of this emerging paradigm. This text covers the full spectrum of granular computing from the basic methodology through algorithms and granular worlds, to a representative spectrum of applications.

Information granules, as encountered in natural language, are implicit in nature. To make them fully operational so they can be effectively used to analyze and design intelligent systems, information granules need to be made explicit. An emerging discipline, granular computing focuses on formalizing information granules and unifying them to create a coherent methodological and developmental environment for intelligent system design and analysis. Granular Computing: Analysis and Design of Intelligent Systems presents the unified principles of granular computing along with its comprehensive algorithmic framework and design practices. Introduces the concepts of information granules, information granularity, and granular computingPresents the key formalisms of information granulesBuilds on the concepts of information granules with discussion of higher-order and higher-type information granulesDiscusses the operational concept of information granulation and degranulation by highlighting the essence of this tandem and its quantification in terms of the associated reconstruction errorExamines the principle of justifiable granularityStresses the need to look at information granularity as an important design asset that helps construct more realistic models of real-world systems or facilitate collaborative pursuits of system modelingHighlights the concepts, architectures, and design algorithms of granular modelsExplores application domains where granular computing and granular models play a visible role, including pattern recognition, time series, and decision makingWritten by an internationally renowned authority in the field, this innovative book introduces readers to granular computing as a new paradigm for the analysis and synthesis of intelligent systems. It is a valuable resource for those engaged in research and practical developments in computer, electrical, industrial, manufacturing, and biomedical engineering. Building from fundamentals, the book is also suitable for readers from nontechnical disciplines where information granules assume a visible position.

“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.

Multi-database mining has been recognized recently as an important and strategically essential area of research in data mining. In this book, we discuss various issues regarding the systematic and efficient development of multi-database mining applications. It explains how systematically one could prepare data warehouses at different branches. As appropriate multi-database mining technique is essential to develop better applications. Also, the efficiency of a multi-database mining application could be improved by processing more patterns in the application. A faster algorithm could also play an important role in developing a better application. Thus the efficiency of a multi-database mining application could be enhanced by choosing an appropriate multi-database mining model, an appropriate pattern synthesizing technique, a better pattern representation technique, and an efficient algorithm for solving the problem. This book illustrates each of these issues either in the context of a specific problem, or in general.

The book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is self-contained, addressing concepts, methodology, algorithms, and case studies and applications, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts: Part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morphology and use in diagnosis; Part II deals with techniques and models of computational intelligence that are suitable for signal processing; and Part III details ECG system-diagnostic interpretation and knowledge acquisition architectures. Illustrative material includes: brief numerical experiments; detailed schemes, exercises and more advanced problems.

Fuzzy Modelling: Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications. Chapters in this book have been written by the leading scholars and researchers in their respective subject areas. Several of these chapters include both theoretical material and applications. The editor of this volume has organized and edited the chapters into a coherent and uniform framework. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, etc., identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models. Fuzzy Modelling: Paradigms and Practice provides a wealth of specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included is a panoply of case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.