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Dynamic Programming
Posted By: mox1x2 | Date: 28 Oct 2007 16:32 | Comments: 2


Richard Bellman, "Dynamic Programming"
Pages: 365 | Publisher: Princeton 1957 | ISBN:069107951X | English | Djvu | 3.3 MB

An introduction to the mathematical theory of multistage decision processes, this text takes a "functional equation" approach to the discovery of optimum policies. The text examines existence and uniqueness theorems, the optimal inventory equation, bottleneck problems in multistage production processes, a new formalism in the calculus of variation, multistage games, and more. 1957 edition. Includes 37 figures







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George J. Klir/Bo Yuan, "Fuzzy Sets and Fuzzy Logic: Theory and Applications"
Publisher: Prentice Hall PTR | ISBN: 0131011715 | 592 Pages | PDF | 2 formats 80 Mb | 12 MB


“Reflecting the tremendous advances that have taken place in the study of fuzzy set theory and fuzzy logic from 1988 to the present, this book not only details the theoretical advances in these areas, but considers a broad variety of applications of fuzzy sets and fuzzy logic as well. Theoretical aspects of fuzzy set theory and fuzzy logic are covered in Part I of the text, including: basic types of fuzzy sets; connections between fuzzy sets and crisp sets; the various aggregation operations of fuzzy sets; fuzzy numbers and arithmetic operations on fuzzy numbers; fuzzy relations and the study of fuzzy relation equations. Part II is devoted to applications of fuzzy set theory and fuzzy logic, including: various methods for constructing membership functions of fuzzy sets; the use of fuzzy logic for approximate reasoning in expert systems; fuzzy systems and controllers; fuzzy databases; fuzzy decision making; and engineering applications. For everyone interested in an introduction to fuzzy set theory and fuzzy logic.”


Book Info :

“Provides the reader with comprehensive coverage of theoretical foundations of fuzzy set theory and fuzzy logic, as well as a broad overview of the increasingly important applications of these novel areas of mathematics. DLC: Fuzzy sets.”


Table of Contents :

“Foreword
Preface
1 From Classical (Crisp) Sets to Fuzzy Sets: A Grand Paradigm Shift 1
2 Fuzzy Sets Versus Crisp Sets 35
3 Operations on Fuzzy Sets 50
4 Fuzzy Arithmetic 97
5 Fuzzy Relations 119
6 Fuzzy Relation Equations 153
7 Possibility Theory 177
8 Fuzzy Logic 212
9 Uncertainty-Based Information 245
10 Constructing Fuzzy Sets and Operations on Fuzzy Sets 280
11 Approximate Reasoning 302
12 Fuzzy Systems 327
13 Pattern Recognition 357
14 Fuzzy Databases and Information Retrieval Systems 379
15 Fuzzy Decision Making 390
16 Engineering Applications 418
17 Miscellaneous Applications 443
Appendix A. Neural Networks: An Overview 467
Appendix B. Genetic Algorithms: An Overview 476
Appendix C. Rough Sets Versus Fuzzy Sets 481
Appendix D. Proofs of Some Mathematical Theorems 484
Appendix E. Glossary of Key Concepts 487
Appendix F. Glossary of Symbols 490
Bibliography 494
Bibliographical Index 548
Name Index 552
Subject Index 563”





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Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Springer| ISBN: 3540787364 | 2008-05-01 | PDF | 248 pages | 6.6 MB

The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means, and hence fuzzy c-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c-means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy c-means become complete by adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two



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Guanrong Chen, Trung Tat Pham, "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems"
CRC | ISBN: 0849316588 | 1 edition | PDF | 328 pages | 2.6 MB

In the early 1970s, fuzzy systems and fuzzy control theories added a new dimension to control systems engineering. From its beginnings as mostly heuristic and somewhat ad hoc, more recent and rigorous approaches to fuzzy control theory have helped make it an integral part of modern control theory and produced many exciting results. Yesterday's "art" of building a working fuzzy controller has turned into today's "science" of systematic design. To keep pace with and further advance the rapidly developing field of applied control technologies, engineers, both present and future, need some systematic training in the analytic theory and rigorous design of fuzzy control systems. Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems provides that training by introducing a rigorous and complete fundamental theory of fuzzy sets and fuzzy logic, and then building a practical theory for automatic control of uncertain and ill-modeled systems encountered in many engineering applications. The authors proceed through basic fuzzy mathematics and fuzzy systems theory and conclude with an exploration of some industrial application examples. Almost entirely self-contained, Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems establishes a strong foundation for designing and analyzing fuzzy control systems under uncertain and irregular conditions. Mastering its contents gives students a clear understanding of fuzzy control systems theory that prepares them for deeper and broader studies and for many practical challenges faced in modern industry.




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Introduction to Fuzzy Logic using MATLAB
Springer; 1 edition | ISBN: 3540357807 | 430 pages | November 16, 2006 | PDF | 27 Mb

Fuzzy Logic, at present is a hot topic, among academicians as well various programmers. This book is provided to give a broad, in-depth overview of the field of Fuzzy Logic. The basic principles of Fuzzy Logic are discussed in detail with various solved examples. The different approaches and solutions to the problems given in the book are well balanced and pertinent to the Fuzzy Logic research projects. The applications of Fuzzy Logic are also dealt to make the readers understand the concept of Fuzzy Logic. The solutions to the problems are programmed using MATLAB 6.0 and the simulated results are given. The MATLAB Fuzzy Logic toolbox is provided for easy reference.

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James C. Bezdek / James Keller / Raghu Krisnapuram / Nikhil R. Pal,
«Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (The Handbooks of Fuzzy Sets)»
Springer | ISBN: 0387245154 | 1 edition (March 25, 2005) | 776 pages | PDF | 40 Mb

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.



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Fuzzy Theory Systems: Techniques and Applications, Four Volume Set
Academic Press | 1999-10-15 | ISBN: 0124438709 | 1777 pages | PDF | 85 Mb

Applications of fuzzy theory (often referred to as "fuzzy logic") are maturing and multiplying at a phenomenal rate, and a comprehensive treatment of these real-world techniques and applications is now very timely. Unlike traditional computer logic involving clear true or false decisions, a fuzzy logic system chooses what is most true after "considering" several contributing and possibly conflicting variables. Examples of practical devices using fuzzy computer decision-making are thermostats that respond to a combination of temperature and humidity (comfort factors), an elevator that considers how crowded a car is rather than just its proximity to the desired floor, and a camera that integrates the variables affecting picture quality. These volumes will present a logical progression from implementation and modeling techniques to industrial/commercial applications to fuzzy neural and adaptive fuzzy systems.


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Fuzzy Control Systems explores one of the most active areas of research involving fuzzy set theory. The contributors address basic issues concerning the analysis, design, and application of fuzzy control systems. Divided into three parts, the book first devotes itself to the general theory of fuzzy control systems. The second part deals with a variety of methodologies and algorithms used in the analysis and design of fuzzy controllers. The various paradigms include fuzzy reasoning models, fuzzy .


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John N. Mordeson, Premchand S. Nair “Fuzzy Graphs and Fuzzy Hypergraphs"
Physica-Verlag Heidelberg | 2000-06-12 | ISBN: 3790812862 | 248 pages | PDF | 8,1 Mb


The authors present an up-to-date account of results from fuzzy graph theory and fuzzy hypergraph theory and give applications of the results. The book should be of interest to research mathematicians and to engineers and computer scientists interested in applications. Some specific application areas presented from fuzzy graph theory are cluster analysis, pattern classfication, database theory, and the problem concerning group structure. Applications of fuzzy hypergraph theory to portfolio management, managerial decision making with an example to waste management, and to neural cell-assemblies are given. It is shown how (fuzzy) hypergraphs and rough sets are related in such a way that ideas may be carried back and forth between the two areas.




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