Simulating Continuous Fuzzy Systems
Buckley, James J.; Jowers, Leonard J. (University of Alabama)
ISBN 10: 3642066682 ISBN 13: 9783642066689
Editorial: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2010
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Descripción
Descripción:
Series: Studies in Fuzziness and Soft Computing. Num Pages: 202 pages, 22 black & white tables, biography. BIC Classification: GPFC; UGK. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 11. Weight in Grams: 338. . 2010. 1st ed. Softcover of orig. ed. 2006. Paperback. . . . . N° de ref. del artículo V9783642066689
Sinopsis:
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.
De la contraportada:
This monograph studies continuous fuzzy dynamical systems using crisp continuous simulation. A crisp continuous dynamical system is presented whose evolution depends on a system of ordinary differential equations (ODEs). The system of ODEs contains parameters many of which have uncertain values. Usually point estimators for these uncertain parameters are used, but the resulting system will not display any uncertainty associated with these estimators. Instead fuzzy number estimators are employed, constructed from expert opinion or from data, for the uncertain parameters. Fuzzy number estimators produce a system of fuzzy ODEs to solve whose solution will be fuzzy trajectories for the variables. The authors use crisp continuous simulation to estimate the trajectories of the support and core of these fuzzy numbers in a variety of twenty applications of fuzzy dynamical systems. The applications range from Bungee jumping to the AIDS epidemic to dynamical models in economics. This book is the companion text to "Simulating Fuzzy Systems" (Springer 2005) which investigated discrete fuzzy systems through crisp discrete simulation.
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Detalles bibliográficos
Título: Simulating Continuous Fuzzy Systems
Editorial: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Año de publicación: 2010
Encuadernación: Encuadernación de tapa blanda
Condición: New
Edición: 1ª Edición
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Simulating Continuous Fuzzy Systems (Paperback)
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Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 3642066682
ISBN 13: 9783642066689
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Paperback
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Paperback. Condición: new. Paperback. This book is the companion text to Simulating Fuzzy Systems which investigated discrete fuzzy systems through crisp discrete simulation. The current book studies continuous fuzzy dynamical systems using crisp continuous simulation. We start with a crisp continuous dynamical system whose evolution depends on a system of ordinary differential equations (ODEs). The system of ODEs contains parameters many of which have uncertain values. Usually point estimators for these uncertain parameters are used, but the resulting system will not display any uncertainty associated with these estimators. Instead we employ fuzzy number estimators, constructed from expert opinion or from data, for the uncertain parameters. Fuzzy number estimators produces a system of fuzzy ODEs to solve whose solution will be fuzzy trajectories for the variables. We use crisp continuous simulation to estimate the trajectories of the support and core of these fuzzy numbers in a variety of twenty applications of fuzzy dynamical systems. The applications range from Bungee jumping to the AIDS epidemic to dynamical models in economics. 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. The system descriptors we want (time in system, number leaving per unit time) will be fuzzy numbers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783642066689
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Imagen de archivo
Simulating Continuous Fuzzy Systems (Paperback)
Publicado por
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 3642066682
ISBN 13: 9783642066689
Nuevo
Paperback
Original o primera edición
Librería: AussieBookSeller, Truganina, VIC, Australia
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. This book is the companion text to Simulating Fuzzy Systems which investigated discrete fuzzy systems through crisp discrete simulation. The current book studies continuous fuzzy dynamical systems using crisp continuous simulation. We start with a crisp continuous dynamical system whose evolution depends on a system of ordinary differential equations (ODEs). The system of ODEs contains parameters many of which have uncertain values. Usually point estimators for these uncertain parameters are used, but the resulting system will not display any uncertainty associated with these estimators. Instead we employ fuzzy number estimators, constructed from expert opinion or from data, for the uncertain parameters. Fuzzy number estimators produces a system of fuzzy ODEs to solve whose solution will be fuzzy trajectories for the variables. We use crisp continuous simulation to estimate the trajectories of the support and core of these fuzzy numbers in a variety of twenty applications of fuzzy dynamical systems. The applications range from Bungee jumping to the AIDS epidemic to dynamical models in economics. 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. The system descriptors we want (time in system, number leaving per unit time) will be fuzzy numbers. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9783642066689
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