By Topic

Dynamic system identification using neural networks

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)
T. Yamada ; NTT Telecommun., Field Syst. R&D Center, Ibaraki ; T. Yabuta

A practical neural network design method for the identification of both the direct transfer function and inverse transfer function of an object plant is proposed. As a practical application of the direct transfer function identifier, a nonlinear plant simulator is also proposed. Simulated and experimental results for a second-order plant show that identification can be satisfactorily achieved and that neural network identifiers can represent nonlinear plant characteristics very well. The characteristics of a neural network direct controller with a feedback control loop, which uses the learning results of the inverse transfer function identifier, is also proposed and confirmed

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:23 ,  Issue: 1 )