Paper Info
Title
An adaptive neuro-fuzzy filter design via periodic fuzzy neural network
Abstract
This paper presents an adaptive filter which uses periodic fuzzy neural network (PFNN) to treat the equalization of nonlinear time-varying channels. The proposed PFNN is based on a neural network learning ability and fuzzy if-then rules structure. In general, training a fuzzy neural network (FNN, or neuro-fuzzy system) to represent some type of plant and system is relatively straightforward and many methods exist. For a given limited amount of information, the PFNN is applied to solve the estimation of the periodic signals. Several examples are shown to illustrate the effectiveness of the proposed approach. The back-propagation learning algorithm with adaptive (or optimal) learning rate is used to speed up the learning. Furthermore, the PFNN is applied to be a nonlinear time-varying channel equalizer with simple structure and fast inference. Efficiency and advantages of the PFNN are verified by these simulations and comparisons.
Year
DOI
Venue
2005
10.1016/j.sigpro.2004.09.011
Signal Processing
Keywords
Field
DocType
nonlinear time-varying channel equalizer,proposed pfnn,periodic signal,periodic fuzzy neural network,neural network,fuzzy neural network,adaptive filter,fuzzy if-then rules structure,nonlinear time-varying channel,neuro-fuzzy system,adaptive neuro-fuzzy filter design,channel equalization,back propagation,filter design,neuro fuzzy,periodic function
Neuro-fuzzy,Nonlinear system,Equalization (audio),Control theory,Fuzzy logic,Artificial intelligence,Adaptive filter,Backpropagation,Artificial neural network,Mathematics,Filter design
Journal
Volume
Issue
ISSN
85
2
Signal Processing
Citations 
PageRank 
References 
5
0.53
12
Authors
2
Name
Order
Citations
PageRank
Ching-Hung Lee159742.31
Yu-Ching Lin238928.19