Modeling of nonlinear systems using the self-organizing fuzzy neural network with adaptive gradient algorithm

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 p...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 266; S. 566 - 578
Hauptverfasser: Han, Hong-Gui, Lin, Zheng-Lai, Qiao, Jun-Fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 29.11.2017
Schlagworte:
Adaptive gradient algorithm
Computational efficiency
Fast convergence
Nonlinear system modeling
Self-organizing fuzzy neural network
Nonlinear system modeling
Fast convergence
Computational efficiency
Self-organizing fuzzy neural network
Adaptive gradient algorithm
ISSN:0925-2312, 1872-8286
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2017.05.065