Paper Info
Title
Cooperative strategy for constructing interval type-2 fuzzy neural network.
Abstract
Interval type-2 fuzzy neural network (IT2FNN) has attracted considerable interest for modeling nonlinear dynamic systems in recent years. However, this promising technique is confronting the problem that constructing a suitable IT2FNN is a potential challenge ignored by most researchers. To solve this problem, a self-constructing interval type-2 fuzzy neural network (SC-IT2FNN), based on the cooperative strategies, is proposed in this paper. The main contributions of this paper are: First, a comprehensive evaluation algorithm (CEA), cooperating with the parameter optimization, is developed to design the structure of SC-IT2FNN to enhance its generalization performance. Second, a hierarchical optimization mechanism, cooperating with the nonlinear and linear parameters of SC-IT2FNN, is proposed to accelerate its learning speed. Third, the convergence of SC-IT2FNN is theoretically analyzed in detail to ensure its successful applications. Finally, several benchmark nonlinear systems and a real application are utilized to evaluate the effectiveness of SC-IT2FNN. The results demonstrate that our proposed SC-IT2FNN significantly improve the modeling performance in terms of high accuracy and compact structure.
Year
DOI
Venue
2019
10.1016/j.neucom.2019.07.004
Neurocomputing
Keywords
Field
DocType
Self-constructing interval type-2 fuzzy neural network,Cooperative strategy,Comprehensive evaluation algorithm,Hierarchical optimization mechanism,Convergence analysis
Convergence (routing),Nonlinear system,Cooperative strategy,Artificial intelligence,Artificial neural network,Mathematics,Nonlinear dynamic systems,Machine learning
Journal
Volume
ISSN
Citations 
365
0925-2312
6
PageRank 
References 
Authors
0.39
0
4
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
Jiaming Li271.09
Xiaolong Wu391.11
Jun-Fei Qiao46915.62