System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

Self-learning fuzzy control strategy of two-layer networked learning control systems based on improved RBF neural network

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
$31 $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

5 Author(s)
Du Dajun ; Dept. of Autom., Shanghai Univ., Shanghai, China ; Li Xue ; Fei Minrai ; Bai Haoliang
more authors

This paper is concerned with two-layer networked learning control system architecture that is consisted of local controller and learning agent. Firstly, networked nondeterministics are tracked respectively by zero order holding (ZOH) and cubic spline interpolator in local controller and learning agent. Then fuzzy control strategy is used in local controller, and an improved radial basis function (RBF) neural network by combing the regularized parameters with the leave-one-out cross-validation criterion is employed in learning agent. Taking advantage of fuzzy control and RBF neural network, a self-learning fuzzy control method is proposed to improve the control performance, where RBF neural network is used to dynamically tune the parameters of local fuzzy controller. Finally, simulation results confirm the effectiveness of the proposed scheme.

Published in:

Control Conference (CCC), 2011 30th Chinese

Date of Conference:

22-24 July 2011