Referenced Items are not available for this document.
Improved predictions can be obtained by using multiple neural networks instead of trying to find a single optimal network as usual. All the component networks of MNN model are selected using generalized information entropy, and the accuracy and reliability of overall model are significantly improved. Based on such an MNN model, a new nonlinear model predictive control algorithm is proposed. Simulation results of a pH CSTR demonstrates that the method is effective and practical
Published in:
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
(Volume:2
)
Date of Conference: 2000
- Page(s):
- 1110 - 1113 vol.2
- Meeting Date :
- 28 Jun 2000-02 Jul 2000
- Print ISBN:
- 0-7803-5995-X
- INSPEC Accession Number:
- 6748303
- Conference Location :
- Hefei
- Digital Object Identifier :
- 10.1109/WCICA.2000.863412
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
