الصفحة 2
الصفحة 2
Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information
This book presents recent advances for imprecise and uncertain engineering information from the point of view of fuzzy database modeling. The topics include fuzzy conceptual data modeling of engineering information, conversion of the fuzzy conceptual models, and database implementation of the fuzzy conceptual data models. Some major data and database models for engineering information modeling are investigated. The main novel aspect of this book is that the book focuses on imprecise and uncertain industrial information modeling viewed from databases and fuzzy database technologies viewed from industrial applications. This may be useful for people involved in theory research, design implementation, and application development of intelligent engineering databases.
Fuzzy Chaotic Systems : Modeling, Control, and Applications
"Fuzzy Chaotic Systems" provides original heuristic research achievements and insightful ideas on the interactions or intrinsic relationships between fuzzy logic and chaos theory. It presents the fundamental concepts of fuzzy logic and fuzzy control, chaos theory and chaos control, as well as thedefinition of chaos on the metric space of fuzzy sets. This monograph discusses and illustrates fuzzy modeling and fuzzy control of chaotic systems, synchronization, anti-control of chaos, intelligent digital redesign, spatiotemporal chaos and synchronization in complex fuzzy systems; as well as a practical application example of fuzzy-chaos-based cryptography. Like other very good books, this book may raise more questions than it can provide answers. It therefore generates a great potential to attract more attention to combine fuzzy systems with chaos theory and contains important seeds for future scientific research and engineering applications.
Fuzzy Applications in Industrial Engineering
After an introductory chapter explaining the recent status of fuzzy sets in IE, this volume involves application chapters on the major seven areas of IE to which fuzzy set theory can contribute. These major application areas are Control and Reliability, Engineering Economics and Investment Analysis, Group and Multi-criteria Decision-making, Human Factors Engineering and Ergonomics, Manufacturing Systems and Technology Management, Optimization Techniques, and Statistical Decision-making. Under these major areas, every chapter includes didactic numerical applications.
Fundamentals of statistics with fuzzy data
This research monograph presents basic foundational aspects for a theory of statistics with fuzzy data, together with a set of practical applications. Fuzzy data are modeled as observations from random fuzzy sets. Theories of fuzzy logic and of random closed sets are used as basic ingredients in building statistical concepts and procedures in the context of imprecise data, including coarse data analysis. The monograph also aims at motivating statisticians to look at fuzzy statistics to enlarge the domain of applicability of statistics in general.
Forging New Frontiers : Fuzzy Pioneers I
The 2005 BISC International Special Event-BISCSE’05 " FORGING THE FRONTIERS" was held in the University of California, Berkeley, “WHERE FUZZY LOGIC BEGAN, from November 3 – 6, 2005. The successful applications of fuzzy logic and it’s rapid growth suggest that the impact of fuzzy logic will be felt increasingly in coming years. Fuzzy logic is likely to play an especially important role in science and engineering, but eventually its influence may extend much farther. In many ways, fuzzy logic represents a significant paradigm shift in the aims of computing - a shift which reflects the fact that the human mind, unlike present day computers, possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain and lacking in categoricity.
Coordination models and languages ; 22nd IFIP WG 6.1 International Conference, COORDINATION 2020, Held as Part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020, Valletta, Malta, June 15–19, 2020, Proceedings
This book constitutes the proceedings of the 22nd International Conference on Coordination Models and Languages, COORDINATION 2020, which was due to be held in Valletta, Malta, in June 2020, as part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020. The conference was held virtually due to the COVID-19 pandemic. The 12 full papers and 6 short papers included in this volume were carefully reviewed and selected from 30 submissions. They are presented in this volume together with 2 invited tutorials and 4 tool papers. The papers are organized in the following topical sections: tutorials; coordination languages; message-based communication; communications: types & implementations; service-oriented computing; large-scale decentralized systems; smart contracts; modelling; verification & analysis.
Computer safety, reliability, and security ; 39th International Conference, SAFECOMP 2020, Lisbon, Portugal, September 16–18, 2020, Proceedings
This book constitutes the proceedings of the 39th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2020, held in Lisbon, Portugal, in September 2020.* The 27 full and 2 short papers included in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections named: safety cases and argumentation; formal verification and analysis; security modelling and methods; assurance of learning-enabled systems; practical experience and tools; threat analysis and risk mitigation; cyber-physical systems security; and fault injection and fault tolerance.
Computational intelligence for modelling and prediction
This book contains recent advances in Computational Intelligence methods for modeling, optimization and prediction and covers a large number of applications. The book presents new Computational Intelligence theory and methods for modeling and prediction. The range of the various applications is captured with 5 chapters in image processing, 2 chapters in audio processing, 3 chapters in commerce and finance, 2 chapters in communication networks and 6 chapters containing other applications.
Computational intelligence based on lattice theory
The emergence of lattice theory within the field of computational intelligence (CI) is partially due to its proven effectiveness in neural computation. Moreover, lattice theory has the potential to unify a number of diverse concepts and aid in the cross-fertilization of both tools and ideas within the numerous subfields of CI. The compilation of this eighteen-chapter book is an initiative towards proliferating established knowledge in the hope to further expand it. This edited book is a balanced synthesis of four parts emphasizing, in turn, neural computation, mathematical morphology, machine learning, and (fuzzy) inference/logic. The articles here demonstrate how lattice theory may suggest viable alternatives in practical clustering, classification, pattern analysis, and regression applications.
Computational intelligence : Principles, techniques and applications
The book Computational Intelligence: Principles, Techniques and Applications presents both theories and applications of Computational Intelligence in a clear, precise and highly comprehensive style. The textbook addresses the fundamental aspects of Fuzzy Sets and Logic, Neural Networks, Evolutionary Computing and Belief Networks. The application areas include Fuzzy Databases, Fuzzy Control, Image Understanding, Expert Systems, Object Recognition, Criminal Investigation, Telecommunication Networks and Intelligent Robots. The book contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own. Emerging areas of Computational Intelligence such as artificial life, particle swarm optimization, artificial immune systems, fuzzy chaos theory, rough sets and granular computing have also been addressed with examples in this book. The book ends with a discussion on a number of open- ended research problems in Computational Intelligence. Graduate students interested to pursue their research in this subject will greatly be benefited with these problems.
Linear Optimization Problems with Inexact Data
Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems—for example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average” values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the literature has not presented a unified theory. Linear Optimization Problems with Inexact Data attempts to present a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.
Learning Classifier Systems ; International Workshops, IWLCS 2003-2005, Revised Selected Papers
The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi?er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005). The Genetic and Evolutionary Computation Conference, the main ACM SIGEvo conference, hosted these three editions.
Knowledge-Driven Computing : Knowledge Engineering and Intelligent Computations
Knowledge-Driven Computing constitutes an emerging area of intensive research located at the intersection of Computational Intelligence and Knowledge Engineering with strong mathematical foundations. It embraces methods and approaches coming from diverse computational paradigms, such as evolutionary computation and nature-inspired algorithms, logic programming and constraint programming, rule-based systems, fuzzy sets and many others. The use of various knowledge representation formalisms and knowledge processing and computing paradigms is oriented towards the efficient resolution of computationally complex and difficult problems.
Classic Works on the Dempster-Shafer Theory of Belief Functions
This book brings together a collection of classic research papers on the Dempster-Shafer theory of belief functions. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions has become a primary tool for knowledge representation and uncertainty reasoning in expert systems.
Applications of Fuzzy Sets Theory ; 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007, Proceedings
The book is organized in topical sections on fuzzy set theory, fuzzy information access and retrieval, fuzzy machine learning, fuzzy architectures and systems; and special sessions on intuitionistic fuzzy sets and soft computing in image processing.
Advances in intelligent computing ; Vol. 3645 ; International conference on intelligent computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part II
This book constitutes the proceedings of the International Conference on Intelligent Computing (ICIC 2005), held in China, 215 papers were published in this book organized into 9 categories, Including Topics Artificial Intelligence Computation by Abstract Devices Algorithm Analysis and Problem Complexity Image Processing and Computer Vision Pattern Recognition Evolutionary Biology
