Paper Info
Title
A self-organizing interval Type-2 fuzzy-neural-network for modeling nonlinear systems.
Abstract
Interval Type-2 fuzzy-neural-network (IT2FNN) has been widely used to model nonlinear systems. In current IT2FNN-based schemes, however, one of the main drawbacks is that the structure of IT2FNN is hard to be determined. In this paper, a self-organizing interval Type-2 fuzzy-neural-network (SOIT2FNN) is introduced via considering the structure adjustment and the parameters learning process simultaneously. Two main contributions of SOIT2FNN are summarized: Firstly, an intensity of information transmission algorithm, which can evaluate the independent component contributions of fuzzy rules, is introduced to optimize the structure of SOIT2FNN. Secondly, an adaptive second-order algorithm, which can obtain fast convergence, is developed to adjust the parameters of SOIT2FNN. To demonstrate the merits of SOIT2FNN, several benchmark nonlinear systems and a real world application are examined with comparisons against other existing methods. Moreover, a statistical analysis of the performance results indicates that the proposed SOIT2FNN performs better and is more suitable for modeling nonlinear systems than some existing methods.
Year
DOI
Venue
2018
10.1016/j.neucom.2018.02.049
Neurocomputing
Keywords
Field
DocType
Nonlinear system modeling,Self-organizing interval Type-2 fuzzy-neural-network,Intensity of information transmission algorithm,Adaptive second-order algorithm
Convergence (routing),Nonlinear system,Fuzzy logic,Algorithm,Information transmission,Artificial intelligence,Artificial neural network,Mathematics,Machine learning,Statistical analysis
Journal
Volume
ISSN
Citations 
290
0925-2312
6
PageRank 
References 
Authors
0.40
33
4
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
Zhi-Yuan Chen260.40
Hong-Xu Liu392.81
Jun-Fei Qiao46915.62