MARC details
| 000 -CABECERA |
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| campo de control de longitud fija |
06453 a2200265 4500 |
| 008 - DATOS DE LONGITUD FIJA--INFORMACIÓN GENERAL |
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| campo de control de longitud fija |
250318s########|||||||||||||||||||||||#d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9688801569 |
| 040 ## - FUENTE DE CATALOGACIÓN |
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| Centro catalogador/agencia de origen |
GAMADERO2 |
| Lengua de catalogación |
spa |
| Centro/agencia transcriptor |
GAMADERO2 |
| 041 ## - CÓDIGO DE IDIOMA |
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| Código de lengua del texto/banda sonora o título independiente |
Inglés |
| 050 00 - SIGNATURA TOPOGRÁFICA DE LA BIBLIOTECA DEL CONGRESO |
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| Número de clasificación |
LCC |
| 100 ## - ENTRADA PRINCIPAL--NOMBRE DE PERSONA |
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| Nombre de persona |
Phillip Kotler |
| 245 ## - MENCIÓN DEL TÍTULO |
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| Título |
Fuzzy Expert Systems / |
| 250 ## - MENCION DE EDICION |
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| Mención de edición |
3 |
| 260 ## - PUBLICACIÓN, DISTRIBUCIÓN, ETC. |
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| Nombre del editor, distribuidor, etc. |
CRC Press |
| Lugar de publicación, distribución, etc. |
EUA |
| Fecha de publicación, distribución, etc. |
1991 |
| 300 ## - DESCRIPCIÓN FÍSICA |
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| Extensión |
307 |
| 490 0# - MENCIÓN DE SERIE |
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| Mención de serie |
Serie |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
TABLE OF CONTENTS<br/><br/>FUZZY EXPERT SYSTEMS THEORY<br/><br/>Chapter 1<br/><br/>The Evolution From Expert Systems to Fuzzy Expert Systems Lawrence O. Hall and Abraham Kandel<br/><br/>Chapter 2<br/><br/>General Purpose Fuzzy Expert Systems Mordechay Schneider and Abraham Kandel<br/><br/>Chapter 3<br/><br/>Inferences With Imprecisions and Uncertainities in Expert Systems Bernadette Bouchon-Meunier<br/><br/>Chapter 4<br/><br/>On the Representation of Relational Production Rules in Expert Systems Ronald R. Yager<br/><br/>Chapter 5<br/><br/>23<br/><br/>43<br/><br/>55<br/><br/>Reduction Procedures for Rule-based Expert Systems as a Tool for Studies of Properties of Expert's Knowledge Antonio Di Nola, Witold Pedrycz, and Salvatore Sessa<br/><br/>69<br/><br/>Chapter 6<br/><br/>The Physiology of the Expert System A. F. Rocha, F. Giorno, B. Leão and A. Theoto<br/><br/>81<br/><br/>Chapter 7<br/><br/>On the Processing of Im…<br/>[APPLICATIONS OF FUZZY EXPERT SYSTEMS<br/><br/>Chapter 11<br/><br/>The Role of Approximate Reasoning in a Medical Expert System D. L. Hudson and M. E. Cohen<br/><br/>165<br/><br/>Chapter 12<br/><br/>Fess: A Reusable Fuzzy Expert System Lawrence O. Hall and Abraham Kandel<br/><br/>181<br/><br/>Chapter 13<br/><br/>Design for Designing: Fuzzy Relational Environmental Design Assistant (FREDA).... Vasco Mancini and Wyllis Bandler<br/><br/>195<br/><br/>Chapter 14<br/><br/>On the Design of a Fuzzy Intelligent Differential Equation Solver Menahem Friedman and Abraham Kandel<br/><br/>203<br/><br/>Chapter 15<br/><br/>MILORD: A Fuzzy Expert Systems Shell R. López de Mántaras, J. Agusti, E. Plaza, and C. Sierra<br/><br/>213<br/><br/>Chapter 16<br/><br/>Medical Decision Making Using Multidimensional Polynomials M. E. Cohen and D. L. Hudson<br/><br/>225<br/><br/>Chapter 17<br/><br/>Fuzzy Expert Systems for an Intelligent Computer-based Tutor Lois W. Hawkes, Sharon J. Derry, and Abraham Kandel 237<br/><br/>Chapter 18<br/><br/>Expert System on a Chip: An Engine for Approximate Reasoning Masaki Togai and Hiroyuki Watanabe<br/><br/>2.59<br/><br/>Chapter 19<br/><br/>A Probabilistic Logic for Expert Systems Arie Tzvieli<br/><br/>275<br/><br/>Chapter 20<br/><br/>COMEX - An Autonomous Fuzzy Expert System for Tactical Communications Networks Mordechay Schneider, Joseph M. Perl, and Abraham Kandel<br/> |
| 520 ## - RESUMEN, ETC. |
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| Resumen, etc. |
PREFACE<br/><br/>This edited volume addresses the specific area of fuzzy expert systems and the choices knowledge engineers and expert systems designers must make to succeed in the field of artificial intelligence To a large extent the field of uncertainty managment, in general, and fuzzy set theory, in particular, is not driven just by an interest in exploring fuzziness and managing uncertainty in what appears to be a vague and imprecise environment, but also by a practical interest in producing better expert systems and knowledge-based systems. In this book we examine the choices within the context of specific and general purpose fuzzy expert systems.<br/><br/>In the first chapter Hall and Kandel identify the basic features of the evolution from expert systems to fuzzy expert systems, investigating the different choices provided by fuzziness. The choices here involve deciding how to handle fuzziness by both machines and humans and how to exploit fuzziness at each level of imprecision. Some of these features may not be visible to the user, but they largely determine the performance of a given expert system implementation. Their selection is based on expected performance benefits vs. the complexity associated with their inclusion. The method of handling imprecision in the system leads us through that evolution to fuzzy expert systems.<br/><br/>In Chapter 2, Schneider and Kandel illustrate the evolutionary process described earlier by discussing the design principles of a general purpose fuzzy expert system using fuzzy expected values and fuzzy expected intervals. It is also shown how to use them as an integral part of the fuzzy expert system.<br/><br/>Chapter 3. by Bouchon-Meunier, discusses inferences with inaccuracy and uncertainty in expert systems. This is the first step in the previously discussed evolution. The knowledge base of an expert system may contain uncertainties and inaccuracies. They are either as-sociated with the inference rules given by the experts or deduced from the observation of facts which do not fit exactly the conditions expressed in the premises of the rules A good<br/><br/>tool to cope with these problems is one of the fuzzy implications introduced in fuzzy logics. In this chapter Bouchon-Meunier indicates some reasons supporting the choice of these inferences and the corresponding combination operator.<br/><br/>The representation within the framework of approximate reasoning of relational type rules is illustrated by Yager in Chapter 4. A relational production rule consists of a rule in which one of the antecedent requirements involves the satisfaction of a relationship between two variables. This concept is used by Yager to represent his view of expert systems and the issues involved in some of their trade-offs.<br/><br/>Chapter 5, by Di Nola, Pedrycz, and Sessa, is devoted to the problem of reduction of the knowledge base in rule-based expert systems. The authors first point out that this problem is placed in the main stream of procedures of expert's knowledge (or its implementation) validation. Subsequently, some algorithms leading to its resolution are given in detail and their complementary character is also underlined. The entire analysis is performed treating the knowledge base in the form of a fuzzy relation, while an inference mechanism is given<br/><br/>by means of fuzzy relation equations. This chapter represents, like the previous chapters, important stages in the evolution of expert systems to fuzzy expert systems.<br/><br/>In Chapter 6, Rocha, Giorno, Lelio, and Theoto use a fuzzy automata model to describe neural networks and perform experiments in the acquisition and use of knowledge hy experts. This is an interesting attempt to shed light on some of the problems of knowledge engineering and presents a set of ideas concerning theoretic vs. expert knowledge.<br/> |
| 526 ## - NOTA DE INFORMACIÓN SOBRE EL PROGRAMA DE ESTUDIO |
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| Program name |
Ingeniería Industrial |
| 942 ## - ELEMENTOS DE ENTRADA SECUNDARIOS (KOHA) |
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| Tipo de ítem Koha |
Libro |
| Fuente del sistema de clasificación o colocación |
Clasificación Decimal Dewey |
| 945 ## - CATALOGADORES |
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| Número del Creador del Registro |
1 |
| Nombre del Creador del Registro |
admin |
| Nombre del último modificador del registro |
Jenny Viridiana Quiroz Linares |
| Número de último modificador del registro |
1261 |
