Hans-Jürgen Zimmermann – författare

This volume contains the proceedings of the Second Joint IFSA-EC and EURO-WGFS Workshop on Progress in Fuzzy Sets in Europe held on April 6 -8, 1989 in Vienna, Austria. The workshop was organized by Prof. Dr. Wolfgang H. Janko from the University of Economics in Vienna under the auspices of IFSA-EC, the European chapter of the International Fuzzy Systems Association, and EURO-WGFS, the working group on Fuzzy Sets of the Association of Eu­ ropean Operational Research Societies. The workshop gathered more than 30 participants coming from Western European countries (Austria, Bel­ gium, England, Germany, Finland, France, Hungary, Italy, Scotland and Spain) Eastern European countries (Bulgaria, the German Federal Repu­ blic, Hungary and Poland) and non-European countries such as China and Japan. The 15 selected and refereed papers included in the volume are in prin­ ciple the author's own versions, with limited editorial changes and small corrections. They are arranged in alphabetical order. I wish to thank all the contributors for their valuable papers and an outstan­ ding cooperation in the editorial project. I also would like to express my sincere thanks to Professor Dr. H. J. Zimmermann for the cooperation in the refereeing procedure.

Since its inception, the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of fuzzy technology can be found in artificial intelligence, computer science, control engineering, decision theory, expert systems, logic, management science, operations research, robotics, and others. Theoretical advances have been made in many directions. The primary goal of Fuzzy Set Theory - and its Applications, Fourth Edition is to provide a textbook for courses in fuzzy set theory, and a book that can be used as an introduction. To balance the character of a textbook with the dynamic nature of this research, many useful references have been added to develop a deeper understanding for the interested reader. Fuzzy Set Theory - and its Applications, Fourth Edition updates the research agenda with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. Chapters have been updated and extended exercises are included.

Advances in Computational Intelligence and Learning: Methods and Applications presents new developments and applications in the area of Computational Intelligence, which essentially describes methods and approaches that mimic biologically intelligent behavior in order to solve problems that have been difficult to solve by classical mathematics. Generally Fuzzy Technology, Artificial Neural Nets and Evolutionary Computing are considered to be such approaches.The Editors have assembled new 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.

Fuzzy Algorithms for Control 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 modeling 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 modeling 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 will therefore be useful for readers from the academic world and also from industry who want to apply fuzzy control in practice.

Since the late 1980s, a large number of very user-friendly tools for fuzzy control, fuzzy expert systems, and fuzzy data analysis have emerged. This has changed the character of this area and started the area of `fuzzy technology'. The next large step in the development occurred in 1992 when almost independently in Europe, Japan and the USA, the three areas of fuzzy technology, artificial neural nets and genetic algorithms joined forces under the title of `computational intelligence' or `soft computing'. The synergies which were possible between these three areas have been exploited very successfully. Practical Applications of Fuzzy Sets focuses on model and real applications of fuzzy sets, and is structured into four major parts: engineering and natural sciences; medicine; management; and behavioral, cognitive and social sciences. This book will be useful for practitioners of fuzzy technology, scientists and students who are looking for applications of their models and methods, for topics of their theses, and even for venture capitalists who look for attractive possibilities for investments.

Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.

In the two decades since its inception by L. Zadeh, the theory of fuzzy sets has matured into a wide-ranging collection of concepts, models, and tech­ niques for dealing with complex phenomena which do not lend themselves to analysis by classical methods based on probability theory and bivalent logic. Nevertheless, a question which is frequently raised by the skeptics is: Are there, in fact, any significant problem areas in which the use of the theory of fuzzy sets leads to results which could not be obtained by classical methods? The approximately 5000 publications in this area, which are scattered over many areas such as artificial intelligence, computer science, control engineering, decision making, logic, operations research, pattern recognition, robotics and others, provide an affirmative answer to this question. In spite of the large number of publications, good and comprehensive textbooks which could facilitate the access of newcomers to this area and support teaching were missing until recently. To help to close this gap and to provide a textbook for courses in fuzzy set theory which can also be used as an introduction to this field, the first volume ofthis book was published in 1985 [Zimmermann 1985 b]. This volume tried to cover fuzzy set theory and its applications as extensively as possible. Applications could, therefore, only be described to a limited extent and not very detailed.

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.

Since the late 1980s, a large number of very user-friendly tools for fuzzy control, fuzzy expert systems, and fuzzy data analysis have emerged. This has changed the character of this area and started the area of `fuzzy technology'. The next large step in the development occurred in 1992 when almost independently in Europe, Japan and the USA, the three areas of fuzzy technology, artificial neural nets and genetic algorithms joined forces under the title of `computational intelligence' or `soft computing'. The synergies which were possible between these three areas have been exploited very successfully. Practical Applications of Fuzzy Sets focuses on model and real applications of fuzzy sets, and is structured into four major parts: engineering and natural sciences; medicine; management; and behavioral, cognitive and social sciences. This book will be useful for practitioners of fuzzy technology, scientists and students who are looking for applications of their models and methods, for topics of their theses, and even for venture capitalists who look for attractive possibilities for investments.

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.

Since the late 1980s, a large number of very user-friendly tools for fuzzy control, fuzzy expert systems, and fuzzy data analysis have emerged. This has changed the character of this area and started the area of `fuzzy technology''. The next large step in the development occurred in 1992 when almost independently in Europe, Japan and the USA, the three areas of fuzzy technology, artificial neural nets and genetic algorithms joined forces under the title of `computational intelligence'' or `soft computing''. The synergies which were possible between these three areas have been exploited very successfully. Practical Applications of Fuzzy Sets focuses on model and real applications of fuzzy sets, and is structured into four major parts: engineering and natural sciences; medicine; management; and behavioral, cognitive and social sciences. This book will be useful for practitioners of fuzzy technology, scientists and students who are looking for applications of their models and methods, for topics of their theses, and even for venture capitalists who look for attractive possibilities for investments.

Die Meinungen darüber, was unter "Operations Research" zu verstehen ist, gehen­ vor allem in Deutschland-auseinander. Es gibt Vertreter der Auffassung, dass "Ope­ rations Research" weitgehend aus den meist mathematischen Modellen, Methoden und Theorien bestehe, die im Laufe der Zeit auf diesem Gebiete entwickelt wor­ den sind. Andere meinen, dass zum "OR" mehr gehöre, nämlich die Art wie man Probleme angehe, der interdisziplinäre Arbeitsstil, die Modellierung von Proble­ men auf verschiedenen Gebieten und unter Umständen sogar die eigentlich in die Informatik gehörende optimale Implementierung von Algorithmen in "tools" oder auf elektronischen Rechenanlagen jeder Art. In Kapitel 1 wird mehr über die ver­ schiedenen Auffassungen gesagt werden. Für dieses Buch ist die Diskussion um das Selbstverständnis des OR von sekundärer Bedeutung, da es sich mit Methoden und Modellen und nicht mit dem Prozess des OR befasst, also mit dem Teil des ORs, der unbestritten ist. Operations Research oder auch Unternehmensforschung, wie dieses Gebiet in Deutschland auch genannt wird, war seit den 50iger Jahren in Deutschland weitge­ hend eine Disziplin mit der man sich primär in der Betriebswirtschaft und später in der Mathematik beschäftigte. Seit geraumer Zeit gewinnt OR auch für Ingenieure, Informatiker, ja selbst Mediziner an Bedeutung. Dies vor allem weil die elektro­ nische Datenverarbeitung in diesen Gebieten Einzug gehalten hat und sich immer stärker zum notwendigen Werkzeug entwickelt. Um aber EDV Anlagen benutzen zu können muss man vorher die relevanten Probleme und Abläufe formal modellieren, und dabei kann OR sehr hilfreich sein.

Probleme, Modelle, Algorithmen Eine zunehmende Anzahl von Autoren [siehe z. B. Boothroyd 1978, S. 110, Müller­ Merbach 1979, Checkland 1983] vertreten die Auffassung, daß es zwei verschiedene Begriffe des Operations Research (OR) gibt: Ein Operations Research aus der Sicht des Praktikers und eins aus der Sicht des Mathematikers. Während das OR aus der Sicht des Praktikers "die modellgestützte Vorbereitung von Entscheidungen zur Gestaltung und Lenkung von Mensch-Maschine-Systemen zur Aufgabe hat" [Müller­ Merbach 1979, S. 295], sieht der Mathematiker das OR "als Teilgebiet der ange­ wandten Mathematik" [Gaede 1974] an. Wie es zu dieser Situation kam, wird näher im Kapitel I dieses Buches beschrieben. Hier wird allerdings die Meinung vertreten, daß es zwar zwei Teile des Gebietes OR geben mag, daß sie jedoch beide wichtig und notwendig für das OR sind und sich aus der Geschichte des OR erklären lassen. Um dies für den Leser leichter verständlich zu machen, ist es nützlich, zunächst auf den hier benutzten Begriff des "Problems" etwas näher einzugehen. Bild 0 skizziert die hier angenommenen Zusammenhänge. Diese Darstellung ist jedoch auf keinen Fall als ein zeitliches Ablaufschema zu interpretieren.

Probleme, Modelle, Algorithmen Eine zunehmende Anzahl von Autoren [siehe z. B. Boothroyd 1978, S. 110, Müller­ Merbach 1979, Checkland 1983] vertreten die Auffassung, daß es zwei verschiedene Begriffe des Operations Research (OR) gibt: Ein Operations Research aus der Sicht des Praktikers und eins aus der Sicht des Mathematikers. Während das OR aus der Sicht des Praktikers "die modellgestützte Vorbereitung von Entscheidungen zur Gestaltung und Lenkung von Mensch-Maschine-Systemen zur Aufgabe hat" [Müller­ Merbach 1979, S. 295], sieht der Mathematiker das OR "als Teilgebiet der ange­ wandten Mathematik" [Gaede 1974] an. Wie es zu dieser Situation kam, wird näher im Kapitel I dieses Buches beschrieben. Hier wird allerdings die Meinung vertreten, daß es zwar zwei Teile des Gebietes OR geben mag, daß sie jedoch beide wichtig und notwendig für das OR sind und sich aus der Geschichte des OR erklären lassen. Um dies für den Leser leichter verständlich zu machen, ist es nützlich, zunächst auf den hier benutzten Begriff des "Problems" etwas näher einzugehen. Bild 0 skizziert die hier angenommenen Zusammenhänge. Diese Darstellung ist jedoch auf keinen Fall als ein zeitliches Ablaufschema zu interpretieren.

In diesem Buch finden Sie die Brücke zwischen klassischem Operations Research und den modernen Gebieten der Heuristik und der Theorie unscharfer Mengen. Klassische und moderne Verfahren und Modelle der Unternehmensforschung sind didaktisch geschickt dargestellt. Das Buch ist entscheidungs- und EDV-orientiert. Mit besonderen Kapiteln über Heuristiken, ganzzahlige Programmierung und die Modellierung schlecht strukturierter Systeme dient es Ihnen als modernes Lehr- und Nachschlagewerk.In der Nachauflage wurde der Text durchgesehen und an einigen Stellen verbessert.