Paper Info
Title
Modeling of nonlinear systems using the self-organizing fuzzy neural network with adaptive gradient algorithm.
Abstract
In this paper, a self-organizing fuzzy neural network with adaptive gradient algorithm (SOFNN-AGA) is proposed for nonlinear systems modeling. First, a potentiality of fuzzy rules (PFR) method is introduced by using the output of normalized layer and the error reduction ratio (ERR) in the training process. And a structure learning approach is developed to determine the network size based on PFR. Second, a novel adaptive gradient algorithm (AGA) with adaptive learning rate is designed to adjust the parameters of SOFNN-AGA. Moreover, a theoretical analysis on the convergence of SOFNN-AGA is given to show the efficiency in both fixed structure and self-organizing structure cases. Finally, to demonstrate the merits of SOFNN-AGA, simulation and experimental results of several benchmark problems and a real world application are examined for nonlinear systems modeling with comparisons against other existing methods. Some promising results are reported in this study, indicating that the proposed SOFNN-AGA performs better favorably in terms of both convergence speed and modeling accuracy.
Year
DOI
Venue
2017
10.1016/j.neucom.2017.05.065
Neurocomputing
Keywords
Field
DocType
Nonlinear system modeling,Self-organizing fuzzy neural network,Adaptive gradient algorithm,Fast convergence,Computational efficiency
Convergence (routing),Network size,Normalization (statistics),Nonlinear system,Computer science,Structure learning,Fuzzy logic,Algorithm,Artificial intelligence,Adaptive neuro fuzzy inference system,Artificial neural network,Machine learning
Journal
Volume
ISSN
Citations 
266
0925-2312
7
PageRank 
References 
Authors
0.44
34
3
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
Zheng-Lai Lin270.44
Jun-Fei Qiao36915.62