simulating continuous fuzzy systems: i

Authors

j. j. buckley

k. d. reilly

l. j. jowers

abstract

in previous studies we first concentrated on utilizing crisp simulationto produce discrete event fuzzy systems simulations. then we extendedthis research to the simulation of continuous fuzzy systems models. in this paperwe continue our study of continuous fuzzy systems using crisp continuoussimulation. consider a crisp continuous system whose evolution depends ondifferential equations. such a system contains a number of parameters thatmust be estimated. usually point estimates are computed and used in themodel. however these point estimates typically have uncertainty associatedwith them. we propose to incorporate uncertainty by using fuzzy numbers asestimates of these unknown parameters. fuzzy parameters convert the crispsystem into a fuzzy system. trajectories describing the behavior of the systembecome fuzzy curves. we will employ crisp continuous simulation to estimatethese fuzzy trajectories. three examples are discussed.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

SIMULATING CONTINUOUS FUZZY SYSTEMS: I

In previous studies we first concentrated on utilizing crisp simulationto produce discrete event fuzzy systems simulations. Then we extendedthis research to the simulation of continuous fuzzy systems models. In this paperwe continue our study of continuous fuzzy systems using crisp continuoussimulation. Consider a crisp continuous system whose evolution depends ondifferential equations. Such a ...

full text

Simulating continuous fuzzy systems

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. To Paula and " the kids " .

full text

Fuzzy stopping problems in continuous-time fuzzy stochastic systems

In a continuous-time fuzzy stochastic system, a stopping model with fuzzy stopping times is presented. The optimal fuzzy stopping times are given under an assumption of regularity for stopping rules. Also, the optimal fuzzy reward is characterized as a unique solution of an optimality equation under a differentiability condition. An example in the Markov models is discussed.

full text

Simulating Continuous Systems with Piecewise-Linear Signals using Time Warp

Recently, an approach using the discrete event paradigm for the simulation of continuous systems has been developed. This approach is based on the use of piecewise-linear approximations for the representation of continuous, time-varying quantities. The focus of this paper is to show how this new technique can be implemented on a multiprocessor. Our hypothesis is that the Time Warp algorithm is ...

full text

designing unmanned aerial vehicle based on neuro-fuzzy systems

در این پایان نامه، کنترل نرو-فازی در پرنده هدایت پذیر از دور (پهپاد) استفاده شده است ابتدا در روش پیشنهادی اول، کنترل کننده نرو-فازی توسط مجموعه اطلاعات یک کنترل کننده pid به صورت off-line آموزش دیده است و در روش دوم یک کنترل کننده نرو-فازی on-line مبتنی بر شناسایی سیستم توسط شبکه عصبی rbf پیشنهاد شده است. سپس کاربرد این کنترل کننده در پهپاد بررسی شده است و مقایسه ای ما بین کنترل کننده های معمو...

My Resources

Save resource for easier access later


Journal title:
iranian journal of fuzzy systems

Publisher: university of sistan and baluchestan

ISSN 1735-0654

volume 2

issue 1 2005

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023