Simulating continuous fuzzy systems / James J. Buckley, Leonard J. Jowers.

"1. 1 Introduction This book is written in two major parts. The ?rst part includes the int- ductory chapters consisting of Chapters 1 through 6. In part two, Chapters 7-26, we present the applications. This book continues our research into simulating fuzzy systems. We started with investigating...

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Bibliographic Details
Main Author: Buckley, James J., 1936-
Corporate Author: SpringerLink ebooks - Engineering (2006)
Other Authors: Jowers, Leonard J.
Format: Ebook
Language:English
Published: Berlin ; New York : Springer, c2006.
Series:Studies in fuzziness and soft computing ; v. 188.
Subjects:
Online Access:Springer eBooks
Description
Summary:"1. 1 Introduction This book is written in two major parts. The ?rst part includes the int- ductory chapters consisting of Chapters 1 through 6. In part two, Chapters 7-26, we present the applications. This book continues our research into simulating fuzzy systems. We started with investigating simulating discrete event fuzzy systems ([7],[13],[14]). These systems can usually be described as queuing networks. Items (transactions) arrive at various points in the s- tem and go into a queue waiting for service. The service stations, preceded by a queue, are connected forming a network of queues and service, until the transaction ?nally exits the system. Examples considered included - chine shops, emergency rooms, project networks, bus routes, etc. Analysis of all of these systems depends on parameters like arrival rates and service rates. These parameters are usually estimated from historical data. These estimators are generally point estimators. The point estimators are put into the model to compute system descriptors like mean time an item spends in the system, or the expected number of transactions leaving the system per unit time. We argued that these point estimators contain uncertainty not shown in the calculations. Our estimators of these parameters become fuzzy numbers, constructed by placing a set of con?dence intervals one on top of another. Using fuzzy number parameters in the model makes it into a fuzzy system. The system descriptors we want (time in system, number leaving per unit time) will be fuzzy numbers."--Publisher's website.
Format:Mode of access: World Wide Web.
Bibliography:Includes bibliographical references and index.
ISBN:1280427701
3540312277
9781280427701
9783540312277
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