Simulating Continuous Fuzzy Systems [electronic resource] / by James J. Buckley, Leonard J. Jowers.

By: Buckley, James J [author.]Contributor(s): Jowers, Leonard J [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Studies in Fuzziness and Soft Computing: 188Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006Description: XII, 202 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540312277Subject(s): Engineering | Artificial intelligence | Mathematics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Applications of Mathematics | Numerical and Computational Methods in EngineeringAdditional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
Contents:
Fuzzy Sets -- Fuzzy Estimation -- Fuzzy Systems -- Continuous Simulation Software -- Simulation Optimization -- Predator/Prey Models -- An Arm’s Race Model -- Bungee Jumping -- Spread of Infectious Disease Model -- Planetary Motion -- Human Cannon Ball -- Electrical Circuits -- Hawks, Doves and Law-Abiders -- Suspension System -- Chemical Reactions -- The AIDS Epidemic -- The Machine/Service Queuing Model -- A Self-Service Queuing Model -- Symbiosis -- Supply and Demand -- Drug Concentrations -- Three Species Competition -- Flying a Glider -- The National Economy -- Sex Structured Population Models -- Summary and Future Research -- Matlab/Simulink Commands for Graphs.
In: Springer eBooksSummary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
No physical items for this record

Fuzzy Sets -- Fuzzy Estimation -- Fuzzy Systems -- Continuous Simulation Software -- Simulation Optimization -- Predator/Prey Models -- An Arm’s Race Model -- Bungee Jumping -- Spread of Infectious Disease Model -- Planetary Motion -- Human Cannon Ball -- Electrical Circuits -- Hawks, Doves and Law-Abiders -- Suspension System -- Chemical Reactions -- The AIDS Epidemic -- The Machine/Service Queuing Model -- A Self-Service Queuing Model -- Symbiosis -- Supply and Demand -- Drug Concentrations -- Three Species Competition -- Flying a Glider -- The National Economy -- Sex Structured Population Models -- Summary and Future Research -- Matlab/Simulink Commands for Graphs.

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.

There are no comments on this title.

to post a comment.

Implemented and Maintained by Biju Patnaik Central Library.
For any Suggestions/Query Contact to library or Email: library@nitrkl.ac.in OR bpcl-cir@nitrkl.ac.in. Ph:91+6612462103
Website/OPAC best viewed in Mozilla Browser in 1366X768 Resolution.

Powered by Koha