Paper Info
Title
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Abstract
Typically, Takagi-Sugeno-Kang (TSK) fuzzy rules have been used as a powerful tool for function approximation problems, since they have the capability of explaining complex relations among variables using rule consequents that are functions of the input variables. But they present the great drawback of the lack of interpretability, which makes them not to be so suitable for a wide range of problems where interpretability of the obtained model is a fundamental key. In this paper, we present a novel approach that extends the work by Bikdash (IEEE Trans. Fuzzy Systems 7 (6) (1999) 686-696), in order to obtain an interpretable and accurate model for function approximation from a set of I/O data samples, which make use of the Taylor Series Expansion of a function around a point to approximate the function using a low number of rules. Our approach also provides an automatic methodology for obtaining the optimum structure of our Taylor series-based (TaSe) fuzzy system as well as its pseudo-optimal rule-parameters (both antecedents and consequents).
Year
DOI
Venue
2005
10.1016/j.fss.2005.01.012
Fuzzy Sets and Systems
Keywords
Field
DocType
accurate model,fuzzy system,fuzzy system model,fuzzy systems,curse of dimensionality,function approximation problem,novel approach,function approximation,system identification,complete rule-based fuzzy systems,fuzzy system models,taylor series expansion,taylor series-based,fuzzy rule,ieee trans,curse of interpretability,taylor series,rule based
Interpretability,Function approximation,Fuzzy logic,Algorithm,Series expansion,Curse of dimensionality,Fuzzy control system,System identification,Mathematics,Taylor series
Journal
Volume
Issue
ISSN
153
3
Fuzzy Sets and Systems
Citations 
PageRank 
References 
32
1.33
26
Authors
5
Name
Order
Citations
PageRank
L. J. Herrera133024.45
H. Pomares272244.28
I. Rojas31750143.09
O. Valenzuela419611.42
A. Prieto518712.72