Analysis of the TaSe-II TSK-Type fuzzy system for function approximation

Authors:
Luis Javier Herrera;Héctor Pomares;Ignacio Rojas;Alberto Guillén;Mohammed Awad;Olga Valenzuela
Affiliations:
Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain
Venue:
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2005

Citing 11
Cited 2

Sugeno type controllers are universal controllers

Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis

Adaptive fuzzy systems and control: design and stability analysis
A characterization of the Hamacher family of t-norms

Fuzzy Sets and Systems
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy

Fuzzy Sets and Systems
A systematic approach to a self-generating fuzzy rule-table forfunction approximation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy sets of rules for system identification

IEEE Transactions on Fuzzy Systems
Improving the interpretability of TSK fuzzy models by combining global learning and local learning

IEEE Transactions on Fuzzy Systems
A highly interpretable form of Sugeno inference systems

IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review

IEEE Transactions on Fuzzy Systems
Structure identification in complete rule-based fuzzy systems

IEEE Transactions on Fuzzy Systems
Comparison of adaptive methods for function estimation from samples

IEEE Transactions on Neural Networks

The TaSe-NF model for function approximation problems: Approaching local and global modelling

Fuzzy Sets and Systems
Interpretable rule extraction and function approximation from numerical input/output data using the modified fuzzy TSK model, tase model

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper reviews and analyzes the performance of the TaSe-II model, carrying out a statistical comparison among different TSK fuzzy system configurations for function approximation. The TaSe-II model, using a special type of rule antecedents, utilizes the Taylor Series Expansion of a function around a point to provide interpretability to the local models in a TSK approximator using a low number of rules. Here we will review the TaSe model basics and endow it with a full learning algorithm for function approximation from a set of I/O data points. Finally we present an ANOVA analysis about the modification of the different blocks that intervene in a TSK fuzzy model whose results support the use of the TaSe-II model.