By Topic

RBF Neural Network Identifier Based on Optimal Selection Cluster Algorithm and PSO Algorithm and its Application

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)
Duan Xiang-jun ; Mech. & Electr. Inst., Nanjing Coll. of Inf. Technol., Nanjing, China ; Wang Yan-Qin

A model of RBF neural network (RBFNN) is framed to solve the problem of identification of nonlinear system. In order to realize the structure identification of RBFNN, a kind of hybrid parameter optimization algorithm is proposed based on optimal selection cluster algorithm and PSO. By this algorithm, it is optimally gained the hidden layer node number of RBFNN in terms of input samples. Then the structure and parameters optimization problem of RBFNN are solved using PSO. The algorithm is used in oilfield volcanic thickness modeling and prediction, results shows the validity of the algorithm.

Published in:

Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on  (Volume:1 )

Date of Conference:

28-29 March 2011