Abstract
Purchase PDF (992 K)
E-mail Article
Add to my Quick Links
Cited By in Scopus (13)
Purchase PDF (344 K)
doi:10.1016/S0925-2312(00)00339-8 
Copyright © 2001 Elsevier Science B.V. All rights reserved.
Using recurrent neuro-fuzzy techniques for the identification and simulation of dynamic systems
Received 21 July 1999;
References and further reading may be available for this article. To view references and further reading you must purchase this article.
Abstract
The identification and simulation of dynamic systems is still a challenging problem. In this article some basic aspects of neuro-fuzzy techniques for the identification and simulation of time-dependent physical systems are presented. In particular, a neuro-fuzzy model that can be used for the identification and the (real-time) simulation of viscoelastic models, is described. The presented model is motivated by a cooperative neuro-fuzzy approach based on a vectorized recurrent neural network architecture. The physical motivation of this model is illustrated and specific propagation procedures and a learning algorithm are presented. Moreover, the usability in practice is demonstrated by an application of the model in the area of surgical simulation.
Author Keywords: System identification; Recurrent network; Neuro-fuzzy; Viscoelastic model; Virtual reality
Corresponding author. Tel.: +49-391-67-11358; fax: +49-391-67-12018; email: andreas.nuernberger@cs.uni-magdeburg.de
