We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Application of RBF neural network based on adaptive hierarchical genetic algorithm in soft sensor modeling

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

2 Author(s)
Na Tang ; Chang Chun Inst. of Opt., Fine Mech. & Phys., Grad. Univ. of Chinese Acad. of Sci., Chang Chun, China ; De-Jiang Zhang

A soft model based on improved RBF neural network (RBFNN) is built in this paper. In order to optimize the RBFNN, an adaptive hierarchical genetic algorithm (AHGA) codes the topology and the parameters together and regards them as one genome to be adjusted dynamically by genetic operations. By searching the excellent genome, the best RBFNN is built. AHGA is more scientific than other methods of setting up the topology based on experiences. The simulation results show that the accuracy and the overall converging speed are really improved. This model, which has good real-time property, good stability and high precision, can be applied to on-line measure the carbon content of molten iron.

Published in:

Natural Computation (ICNC), 2011 Seventh International Conference on  (Volume:1 )

Date of Conference:

26-28 July 2011