Journal
NEUROCOMPUTING
Volume 290, Issue -, Pages 196-207Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2018.02.049
Keywords
Nonlinear system modeling; Self-organizing interval Type-2; fuzzy-neural-network; Intensity of information transmission algorithm; Adaptive second-order algorithm
Categories
Funding
- National Science Foundation of China [61622301, 61533002]
- Beijing Natural Science Foundation [4172005]
- Major National Science and Technology Project [2017ZX07104]
Ask authors/readers for more resources
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. (c) 2018 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available