A basic approach to reduce the complexity of a self-generated fuzzy rule-table for function approximation by use of symbolic regression in 1d and 2d cases

Authors:
G. Rubio;H. Pomares;I. Rojas;A. Guillen
Affiliations:
Department of Computer Architecture and Computer Technology, University of Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Spain
Venue:
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Year:
2005

Citing 5
Cited 1

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Fuzzy and Neural Hybrid Expert Systems: Synergetic AI

IEEE Expert: Intelligent Systems and Their Applications
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

Creation of Specific-to-Problem Kernel Functions for Function Approximation

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

There are many papers in the literature that deal with the problem of the design of a fuzzy system from a set of given training examples. Those who get the best approximation accuracy are based on TSK fuzzy rules, which have the problem of not being as interpretable as Mamdany-type Fuzzy Systems. A question now is posed: How can the interpretability of the generated fuzzy rule-table base be increased? A possible response is to try to reduce the rule-base size by generalizing fuzzy-rules consequents which are symbolic functions instead of fixed scalar values or polynomials, and apply symbolic regressions technics in fuzzy system generation. A first approximation to this idea is presented in this paper for 1-D and 2D functions.