Paper Info
Title
Self-organization of a recurrent RBF neural network using an information-oriented algorithm.
Abstract
This paper investigates how to construct a recurrent radial basis function neural network (RRBFNN) by an information-oriented algorithm (IOA) and how to adjust the parameters by a gradient algorithm simultaneously. In this IOA-based RRBFNN (IOA-RRBFNN), the proposed IOA is used to calculate the information processing strength (IPS) of hidden neurons, such that the independent component contributions between the hidden neurons and output neurons can be extracted. Then, a novel self-organizing strategy is proposed to optimize the structure of RRBFNN based on the input IPS and output IPS of hidden neurons. Meanwhile, a gradient algorithm is developed to update the parameters of IOA-RRBFNN. The proposed IOA-RRBFNN can be used to organize the network structure and adjust the parameters to improve its performance. Finally, several examples are presented to illustrate the effectiveness of IOA-RRBFNN. The results demonstrate that the proposed IOA-RRBFNN is more competitive in solving the nonlinear system modeling problems compared with some existing methods.
Year
DOI
Venue
2017
10.1016/j.neucom.2016.11.010
Neurocomputing
Keywords
Field
DocType
Information-oriented algorithm,Recurrent radial basis function neural network,Information processing strength,Component contributions
Data mining,Nonlinear system,Computer science,Radial basis function neural,Self-organization,Artificial intelligence,Artificial neural network,Imagination,Network structure,Search engine,Information processing,Pattern recognition,Algorithm,Machine learning
Journal
Volume
Issue
ISSN
225
C
0925-2312
Citations 
PageRank 
References 
3
0.38
53
Authors
3
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
Ya-Nan Guo230.38
Jun-Fei Qiao36915.62