International Series in Intelligent Technologies – serie

The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.

This research monograph presents the fundamentals of fuzzy control theory and recent results in extending fuzzy control theory for multivariable and large-scale systems. Among the topics presented are: the problem of dimensional reduction of fuzzy relations; the possibilities for decomposing multivariable systems for the purpose of distributed fuzzy control; a method of two-level hierarchical fuzzy control, based on local and global control components; methods of decentralized fuzzy control by different types of spatial decomposition of the original system into subsystems, and, analogously, methods of multilayer fuzzy control on the basis of several types of temporal decomposition of the original system into layers. The theoretical results are illustrated by numerical examples and two case studies. It is shown that the number of fuzzy relations, respectively on-line computations, can be significantly reduced and in this way real-time control implementation can be facilitated. The book is written using a structured method-algorithm-example format, and offers solutions to existing problems, as well as pointing a way to further investigations.

Computational intelligence paradigms have attracted the growing interest of researchers, scientists, engineers and application engineers in a number of everyday applications. These applications are not limited to any particular field and include engineering, business, banking and consumer electronics. Computational intelligence paradigms include artificial intelligence, artificial neural networks, fuzzy systems and evolutionary computing. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimization applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge. "Practical Applications of Computational Intelligence Techniques" contains 12 chapters providing actual application of these techniques in the real world. Such examples include, but are not limited to, intelligent household appliances, aerial spray models, industrial applications and medical diagnostics and practice.This book should be useful to researchers, practicing engineers/scientists and students, who are interested in developing practical applications in a computational intelligence environment.

This text focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the entire process of dynamic pattern recognition. It sets a general framework for Dynamic Pattern Recognition, describing in detail the monitoring process using fuzzy tools and the adaptation process in which the classifiers have to be adapted, using the observations of the dynamic process. It then focuses on the problem of a changing cluster structure (new clusters, merging of clusters, splitting of clusters and the detection of gradual changes in the cluster structure). Finally, the book integrates these parts into a complete algorithm for dynamic fuzzy classifier design and classification.

This work presents developments and applications in the area of computational intelligence, which essentially describes methods and approaches that mimic biologically intelligent behaviour in order to solve problems that have been difficult to solve by classical mathematics. Fuzzy technology, artificial neural nets and evolutionary computing are considered to be such approaches. The Editors have assembled contributions in the areas of fuzzy sets, neural sets and machine learning, as well as combinations of them (so-called hybrid methods) in the first part of the book. The second part of the book is dedicated to applications in the areas that are considered to be most relevant to computational intelligence.

The field of "intelligent interfaces and systems" has witnessed a rapid growth during the last decade of the 20th century. An impressive number of papers, conference tutorials, and volumes have been devoted to the topic. In 1990, intelligent systems constituted a rather exotic topic and many were skeptical as to whether such systems would amount to more than a nice name. Nowadays, intelligent systems represent a powerful tool in many applications, in all industrial fields. Their development evolved on both the horizontal dimension, with a constantly increasing number of applications, and on the vertical dimension, by including more capabilities ranging from sensoric to neurofuzzy systems, intelligent agents, speech and image understanding, and decision-making in complex environments. Representing a comprehensive coverage of the field of intelligent systems and interfaces, this text includes information about fundamental aspects, software-, sensors-, and hardware-related issues.Moreover, the contributors to this volume offer, beyond a systematic overview of intelligent interfaces and systems, deep, practical knowledge in building and using intelligent systems in various applications. Special emphasis is placed on specific aspects and requirements in applications. The text is a tool for the scientific community in all areas of applied intelligent technologies. The chapters are written by a selected pool of experts in the field of intelligent systems. The contributors thoroughly review the state of the art, explain the problems to be addressed and show how these problems can be solved. Extensive references are included, offering the reader a perspective on the currently available literature and trends.

This text examines the use of analytical tools developed for studying uncertainty analysis in engineering, control systems, and the sciences. It is the work of 38 contributors who have each written chapters on developed analytical methods - fuzzy logic, neural networks, simulation, and Bayesian techniques - and have applied them to uncertainty phenomena arising out of information and knowledge problems in the fields of engineering and the sciences. The book is divided into the following parts : part I reports the theoretical studies on uncertainty types, models and measures; part II reviews the applications of uncertain theoretical tools to engineering systems; part III describes the methodologies of fuzzy-neural data analysis and forecasting; part IV presents two chapters on fuzzy-neuro systems; and part V describes the methodologies for fuzzy decision making and optimization and their computational methods. The editors also provide a concluding chapter on uncertainty and uncertainty modelling.

Rule-based fuzzy modelling has been recognized as a powerful technique for the modelling of partly-known nonlinear systems. Fuzzy models can effectively integrate information from different sources, such as physical laws, empirical models, measurements and heuristics. Application areas of fuzzy models include prediction, decision support, system analysis and control design. The text addresses fuzzy modeling from the systems and control engineering points of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models. To automatically generate fuzzy models from measurements, a comprehensive methodology is developed which employs fuzzy clustering techniques to partition the available data into subsets characterized by locally linear behaviour. The relationships between the presented identification method and linear regression are exploited, allowing for the combination of fuzzy logic techniques with standard system identification tools.Attention is paid to the trade-off between the accuracy and transparency of the obtained fuzzy models. Control design based on a fuzzy model of a nonlinear dynamic process is addressed, using the concepts of model-based predictive control and internal model control with an inverted fuzzy model. To this end, methods to exactly invert specific types of fuzzy models are presented. In the context of predictive control, branch-and-bound optimization is applied. The main features of the presented techniques are illustrated by means of examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.

The aim of this book is to acquaint the reader with the basic elements of fuzzy set theory, fuzzy logic, fuzzy logic systems, artificial neural networks, neurofuzzy modelling, and applications of fuzzy logic and neural networks in traffic and transportation engineering. It also indicates directions for future research in this area.

This text gives an overview of the research results of a number of European research groups that are active and play a leading role in the field of fuzzy modelling and control. It contains 12 chapters divided into three parts. Chapters in the first part address the position of fuzzy systems in control engineering and in the AI community. State-of-the-art surveys on fuzzy modelling and control are presented along with a critical assessment of the role of these methodologists in control engineering. The second part is concerned with several analysis and design issues in fuzzy control systems. The analytical issues addressed include the algebraic representation of fuzzy models of different types, their approximation properties, and stability analysis of fuzzy control systems. Several design aspects are addressed, including performance specification for control systems in a fuzzy decision-making framework and complexity reduction in multivariable fuzzy systems. In the third part of the book, a number of applications of fuzzy control are presented.It is shown that fuzzy control in combination with other techniques such as fuzzy data analysis is an effective approach to the control of modern processes which present many challenges for the design of control systems. One has to cope with problems such as process nonlinearity, time-varying characteristics for incomplete process knowledge. Examples of real-world industrial applications presented in this book are a blast furnace, a lime kiln and a solar plant. Other examples of challenging problems in which fuzzy logic plays an important role and which are included in this book are mobile robotics and aircraft control. The aim of this book is to address both theoretical and practical subjects in a balanced way. It should therefore be useful for readers from the academic world and also from industry who want to apply fuzzy control in practice.

Fuzzy logic is "a recent revolutionary technology" which has brought together researchers from mathematics, engineering, computer science, cognitive and behavioral sciences, etc. The work in fuzzy technology at the Laboratory for International Fuzzy Engineering (LIFE) has been specifically applied to engineering problems. This book reflects the results of the work that has been undertaken at LIFE with chapters treating the following topical areas: Decision Support Systems, Intelligent Plant Operations Support, Fuzzy Modeling and Process Control, System Design, Image Understanding, Behaviour Decisions for Mobile Robots, the Fuzzy Computer, and Fuzzy Neuro Systems. The book is an analysis of research which has been implemented in the areas of fuzzy engineering technology. The analysis can be used to improve these specific applications or, perhaps more importantly, to investigate more sophisticated fuzzy control applications.

Fuzzy knowledge and fuzzy systems affect our lives today as systems enter the world of commerce. Fuzzy systems are incorporated in domestic appliances (washing machine, air conditioning, microwave, telephone) and in transport systems (a pilotless helicopter has recently completed a test flight). Future applications are expected to have dramatic implications for the demand for labour, among other things. It was with such thoughts in mind that this international survey of future applications of fuzzy logic has been undertaken. The results are likely to be predictive for a decade beyond the millenium. The predictive element is combined with a bibliography which serves as an historical anchor. This text aims to be a milestone in the development of fuzzy logic and applications of three representative subsystems: fuzzy control, fuzzy pattern recognition and fuzzy communications.

One of the attractions of fuzzy logic is its utility in solving many real engineering problems. The major obstacles in building a real intelligent machine involve dealing with random disturbances, processing large amounts of imprecise data, interacting with a dynamically changing environment, and coping with uncertainty. Neural-fuzzy techniques help one to solve many of these problems.

This text contains contributions by many of the experts in the field, including coverage of the mathematical foundations of the theory, decision making and systems science. The book examines: fuzzy set theory and its evolution in mathematics; results on foundations of fuzzy set theory; decision making and systems science; and fuzzy control and neural systems.

This volume presents the results on the use of fuzzy set theory to represent imprecision in databases. It provides background material on fuzzy sets and database theory, covering all of the major approaches and models of fuzzy databases that have been developed, including coverage of commercial/industrial systems and applications. Background and introductory material are provided in the first two chapters. The major approaches in fuzzy databases comprise the second part of the volume. This includes the use of similarity and proximity measures as the fuzzy techniques used to extend the relational data modelling and the use of possibility theory approaches in the relational model. Coverage includes extensions to the data model, querying approaches, functional dependencies and other topics including implementation issues, information measures, database security, alternative fuzzy data models, the IFO model, and the network data models. A number of object-oriented extensions are also discussed. The use of fuzzy data modelling in geographical information systems (GIS) and use of rough sets in rough and fuzzy rough relational data models are presented.Major emphasis has been given to applications and commercialization of fuzzy databases. Several specific industrial/commercial products and applications are described. These include approaches to developing fuzzy front-end systems and special-purpose systems incorporating fuzziness.

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.

This text is an edited collection of contributed chapters covering basic fuzzy logic theory, fuzzy linear programming, and applications. Special emphasis has been given to coverage of recent research results, and to industrial applications of fuzzy logic. The chapters are works that have been written for this book by researchers in this field. The authors have indicated direct links between fuzzy set theory and its industrial applications. This work provides researchers and industrial engineers with up-to-date coverage of new results on fuzzy logic and relates these results to their industrial use.

This work presents some of the most recent research results that apply fuzzy sets to engineering design and configuration problems. These chapters cover the representation and manipulation of imprecise information during the synthesis of new designs and selection of configurations. The authors utilize the mathematics of fuzzy sets to represent information that has not yet been reduced to precise descriptions. In most cases, the mathematics of probability is also used to represent more traditional stochastic uncertainties such as uncontrolled manufacturing variations, etc. Presentations on fuzzy optimization and AHP methods are also included. These (fuzzy) set-based methods have particular application to concurrent engineering techniques.

This work focuses on consensus formation in multiperson decision-making groups using imprecise information. The editors have solicited and organized contributions on this subject from leading experts in the field. The contributors include Ronald Yager, Henri Prade, George Klier, and Janos Fodor, among others. These contributions are concerned with issues related to modelling and monitoring of consensus-reaching processes under fuzzy preferences and fuzzy majorities. The chapters include an array of paradigms, tools and techniques that can help develop new analytical tools for consensus-reaching processes.

Uncertainty has been a concern to engineers, managers, and scientists for many years in engineering and sciences. Uncertainty has for a long time been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous and more tools that model uncertainty than statistics have been proposed by several engineers and scientists. The tool/ method to model uncertainty in a specific context should really be choices by considering the features of the phenomenon under consideration not independently of what is known about the system and what causes uncertainty. Applied Research in Uncertainty Modeling Analysis concentrates on general aspects of uncertainty, modeling, and methods, and consists of large numbers of examples on engineering and sciences.

This volume presents the results of approximately 15 years of work from researchers around the world on the use of fuzzy set theory to represent imprecision in databases. The maturity of the research in the discipline and the recent developments in commercial/industrial fuzzy databases provided an opportunity to produce this survey. In this introduction we will describe briefly how fuzzy databases fit into the overall design of database systems and then overview the organization of the text. FUZZY DATABASE LANDSCAPE The last five years have been witness to a revolution in the database research community. The dominant data models have changed and the consensus on what constitutes worthwhile research is in flux. Also, at this time, it is possible to gain a perspective on what has been accomplished in the area of fuzzy databases. Therefore, now is an opportune time to take stock of the past and establish a framework. A framework should assist in evaluating future research through a better understanding of the different aspects of imprecision that a database can model [ 1 l.

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.

Fuzzy Logic Foundations and Industrial Applications is an organized edited collection of contributed chapters covering basic fuzzy logic theory, fuzzy linear programming, and applications. Special emphasis has been given to coverage of recent research results, and to industrial applications of fuzzy logic. The chapters are new works that have been written exclusively for this book by many of the leading and prominent researchers (such as Ronald Yager, Ellen Hisdal, Etienne Kerre, and others) in this field. The contributions are original and each chapter is self-contained. The authors have been careful to indicate direct links between fuzzy set theory and its industrial applications. Fuzzy Logic Foundations and Industrial Applications is an invaluable work that provides researchers and industrial engineers with up-to-date coverage of new results on fuzzy logic and relates these results to their industrial use.

As understanding of the engineering design and configuration processes grows, the recognition that these processes intrinsically involve imprecise information is also growing. This book collects some of the most recent work in the area of representation and manipulation of imprecise information during the syn­ thesis of new designs and selection of configurations. These authors all utilize the mathematics of fuzzy sets to represent information that has not-yet been reduced to precise descriptions, and in most cases also use the mathematics of probability to represent more traditional stochastic uncertainties such as un­ controlled manufacturing variations, etc. These advances form the nucleus of new formal methods to solve design, configuration, and concurrent engineering problems. Hans-Jurgen Sebastian Aachen, Germany Erik K. Antonsson Pasadena, California ACKNOWLEDGMENTS We wish to thank H.-J. Zimmermann for inviting us to write this book. We are also grateful to him for many discussions about this new field Fuzzy Engineering Design which have been very stimulating. We wish to thank our collaborators in particular: B. Funke, M. Tharigen, K. Miiller, S. Jarvinen, T. Goudarzi-Pour, and T. Kriese in Aachen who worked in the PROKON project and who elaborated some of the results presented in the book. We also wish to thank Michael J. Scott for providing invaluable editorial assis­ tance. Finally, the book would not have been possible without the many contributions and suggestions of Alex Greene of Kluwer Academic Publishers. 1 MODELING IMPRECISION IN ENGINEERING DESIGN Erik K. Antonsson, Ph.D., P.E.

Fuzzy Set Theory and Advanced Mathematical Applications contains contributions by many of the leading experts in the field, including coverage of the mathematical foundations of the theory, decision making and systems science, and recent developments in fuzzy neural control. The book supplies a readable, practical toolkit with a clear introduction to fuzzy set theory and its evolution in mathematics and new results on foundations of fuzzy set theory, decision making and systems science, and fuzzy control and neural systems. Each chapter is self-contained, providing up-to-date coverage of its subject. Audience: An important reference work for university students, and researchers and engineers working in both industrial and academic settings.

Fuzzy logic is `a recent revolutionary technology' which has brought together researchers from mathematics, engineering, computer science, cognitive and behavioral sciences, etc. The work in fuzzy technology at the Laboratory for International Fuzzy Engineering (LIFE) has been specifically applied to engineering problems. This book reflects the results of the work that has been undertaken at LIFE with chapters treating the following topical areas: Decision Support Systems, Intelligent Plant Operations Support, Fuzzy Modeling and Process Control, System Design, Image Understanding, Behavior Decisions for Mobile Robots, the Fuzzy Computer, and Fuzzy Neuro Systems. The book is a thorough analysis of research which has been implemented in the areas of fuzzy engineering technology. The analysis can be used to improve these specific applications or, perhaps more importantly, to investigate more sophisticated fuzzy control applications.

The field of "intelligent interfaces and systems" has seen a fast growth last decade. An impressive number of papers, conference tutorials, and volumes were devoted to the topic. Ten years ago, intelligent systems constituted a rather exotic topic and many were skeptic that such systems amount to more than a nice name. Nowadays, intelligent systems represent a powerful tool in many applications, in all industrial fields. Their development evolved on both the horizontal dimension, with a constantly increasing number of applications, and on the vertical dimension, by including more capabilities going from sensoric to neurofuzzy systems, intelligent agents, speech and image understanding, and decision making in complex environments. The domain constituted by the intelligent systems is now too large to be covered in a single volume. Consequently, the Editors considered a balance between several selected sub-fields, to insure the unity of the volume, yet allowing a large enough horizon and a consistent understanding of the entire field, including real-life applications. The volume represents a comprehensive coverage of the field, including fundamental aspects, software-, sensors-, and hardware-related issues. Moreover, the contributors to this volume offer, beyond a systematic overview of intelligent interfaces and systems, deep, practical knowledge in building and using intelligent systems in various applications. A special emphasis is on specific aspects and requirements in applications. In addition, the second Editor proposes two chapters addressing the management of projects dealing with intelligent systems.

Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.

We live, unfortunately, in turbulent and difficult times plagued by various political, economic, and social problems, as well as by natural disasters worldwide. Systems become more and more complicated, and this concerns all levels, exemplified first by global political alliances, groups of countries, regions, etc., and secondly, by multinational (global) corporations and companies of all sizes. These same concerns affect all social groups. This all makes decision processes very complicated. In virtually all decision processes in these complicated systems, there are various actors (decision makers) who represent individual subjects (persons, countries, companies, etc.) and their respective interest groups. To reach a meaningful (good) decision, opinions of all such actors must be taken into account or a given decision may be rejected and not implemented. Ideally, a decision would be made after a consensus between the parties involved had been attained. So, consensus is a very desirable situation. In most real-world cases there is considerable uncertainty concerning all aspects of the decision making process. Moreover, opinions, goals, constraints, etc. are usually imprecisely known. This makes the decision making process difficult as one cannot employ conventional "hard" tools.