Paper Info
Title
Type-2 Fuzzy Neuro System Via Input-to-State-Stability Approach
Abstract
This paper proposes the type-2 fuzzy neural network system (type-2 FNN) which combines the advantages of type-2 fuzzy logic systems (FLSs) and neural networks (NNs). For considering the system uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The previous results of type-1 FNN systems can be extended to a type-2 one. Furthermore, the corresponding learning algorithm is derived by input-to-state-stability (ISS) approach. Nonlinear system identification is presented to illustrate the effectiveness of our approach.
Year
DOI
Venue
2007
10.1007/978-3-540-72393-6_39
ISNN (2)
Keywords
Field
DocType
via input-to-state-stability approach,system uncertainty,type-2 fuzzy neuro system,neural network,type-2 fuzzy neural network,type-2 fnn,nonlinear system identification,type-2 fnn system,corresponding learning algorithm,type-1 fnn system,type-2 fuzzy logic system,type-2 flss,fuzzy neural network
Neuro-fuzzy,Fuzzy classification,Fuzzy set operations,Computer science,Fuzzy logic,Nonlinear system identification,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Artificial neural network,Machine learning
Conference
Volume
ISSN
Citations 
4492
0302-9743
1
PageRank 
References 
Authors
0.35
8
2
Name
Order
Citations
PageRank
Ching-Hung Lee159742.31
Yu-Ching Lin238928.19