… "if-then" rules that are easy to understand, verify, and extend. This paper presents methods
for extracting fuzzy rules for both function approximation and pattern classification. The rule …

… best fuzzy rules cover the extrema or bumps in the function. … systems can approximate the
unknown fuzzy rules from … these rules and add rules to improve the function approximation. We …

… In this paper we extend the method to function approximation. For … Additionally, we
compare the approximation performance of the fuzzy system with the function approximation …

… ) should be selected to approximate the function, from the possible configurations provided
by the algorithm. If we intend the data to be accurately approximated by the fuzzy rules, from …

… a function approximation method based on fuzzy rules … prior to extraction of fuzzy rules
can improve the performance … We applied the concept of partition to function approximation …

… of such function approximation: the fuzzy systems can uniformly approximate any polynomials
… in this paper) in the sense that these fuzzy systems could approximate any real continuous …

… of both fuzzy logic … fuzzy neurons and fuzzy weights) have been presented, the typical
approach of FNN’s is to build standard neural networks which are designed to approximate a fuzzy …

… functions with many inputs by fuzzy rule-based systems. That is, our task is to describe a
multi-input nonlinear function by fuzzy if-then rules … function), we usually write linguistic rules in a …

… functions to approximate different functions. We scored each test in terms of the mean-squared
error (MSE) of the function approximation … We tested the adaptive set functions on more …

… forward fuzzy systems approximate continuous functions. We ex… fuzzy systems approximate
a battery of test functions. No one set shape emerges as the best shape. The sinc function …