Paper Info
Title
Signal modeling with dynamic fuzzy sets.
Abstract
Signals originating from a class of time-varying systems are modeled as dynamic fuzzy sets, i.e. fuzzy sets with membership functions that change in time. A signal trajectory in feature space is mapped into a dynamic fuzzy set which quantifies and characterizes the most significant aspects of the system's dynamics. A dynamic fuzzy set is visualized as a trajectory within a corresponding fuzzy information space. An example involving modeling of electroencephalographic signals during sleep is presented to illustrate the applicability of the method.
Year
DOI
Venue
1996
10.1109/ICASSP.1996.550142
ICASSP
Keywords
Field
DocType
signal modeling,fuzzy set,corresponding fuzzy information space,dynamic fuzzy set,membership function,time-varying system,electroencephalographic signal,significant aspect,feature space,signal trajectory,surgery,fuzzy sets,electroencephalography,system dynamics,eeg,differential equations,sleep,visualization,fuzzy set theory,signal processing,trajectory
Neuro-fuzzy,Pattern recognition,Fuzzy classification,Defuzzification,Computer science,Fuzzy logic,Fuzzy set,Artificial intelligence,Fuzzy control system,Fuzzy associative matrix,Fuzzy number
Conference
ISBN
Citations 
PageRank 
0-7803-3192-3
0
0.34
References 
Authors
3
3
Name
Order
Citations
PageRank
B. R. Kosanovic100.34
L. F. Chaparro24511.06
R. J. Sclabassi35010.81