By Topic

Applications of neural networks in learning of dynamical systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
S. R. Chu ; Sch. of Mech. Eng., Purdue Univ., West Lafayette, IN, USA ; R. Shoureshi

One of the immediate applications of neural networks in the engineering field is pattern recognition and its extension to system identification. Three unique features of neural networks, namely, learning, high-speed processing of massive data, and the ability to handle signals with degrees of uncertainty, make such networks attractive to dynamical systems. The first step in analyzing such systems is to learn the dynamics of the system, i.e., system identification. A time-domain approach using a Hopfield network and a frequency-domain approach using spectral decomposition for identification of dynamical systems are presented. Simulation results are discussed

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:22 ,  Issue: 1 )