Paper Info
Title
Nonlinear Channel Equalization Using A Novel Recurrent Interval Type-2 Fuzzy Neural System
Abstract
Nonlinear inter-symbol interference leads to significant error rate in nonlinear communication and digital storage channel. In this paper, therefore, a novel recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A) is proposed for nonlinear channel equalization. The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the fuzzy logic system in a five-layer neural network structure. The RT2FNN-A is an extensive results of type-2 fuzzy neural network to provide memory elements for capturing the system's dynamic information and has the properties of high approximation accuracy and small network structure. Based on the Lyapunov theorem and gradient descent method, the convergence of RT2FNN-A is guaranteed and the corresponding learning algorithm is derived. In addition, the RT2FNN-A is applied in the nonlinear channel equalization to show the performance and effectiveness of RT2FNN-A system.
Year
Venue
Keywords
2009
ENGINEERING LETTERS
type-2 fuzzy logic system, recurrent neural network, asymmetric membership functions, channel equalization
Field
DocType
Volume
Convergence (routing),Gradient descent,Nonlinear system,Computer science,Control theory,Communication channel,Fuzzy set,Time delay neural network,Artificial intelligence,Adaptive neuro fuzzy inference system,Artificial neural network,Machine learning
Journal
17
Issue
ISSN
Citations 
2
1816-093X
0
PageRank 
References 
Authors
0.34
17
3
Name
Order
Citations
PageRank
Ching-Hung Lee159742.31
Tzu Wei Hu200.34
Hao Han Chang300.34