This paper investigates function approximation on discrete input spaces by both neural networks and neural-fuzzy systems. Rather than use existing neural networks for function approximation on continuous input spaces, this paper proposes, based on a hierarchical systematic perspective, four simplified approximation schemes: simplified neural networks, extended simplified neural networks, simple...

Many researchers have been interested in approximation properties of fuzzy logic systems (FLS), which like neural networks can be seen as approximation schemes. Almost all of them tackled Mamdani fuzzy model, which was shown to have many interesting features. This paper aims to present alternatives for traditional inference mechanisms and CRI method. The most attractive advantage of these new m...

Fuzzy systems can be used for function approximation based on a set of linguistic rules. We present a method to obtain the necessary parameters for such a fuzzy system by a neuro-fuzzy training method. The learning algorithm is able to determine the structure and the parameters of a fuzzy system from sample data. The approach is an extension to our already published NE-FCON and NEFCLASS models ...

Many researchers have been interested in approximation properties of fuzzy logic systems (FLS), which like neural networks can be seen as approximation schemes. Almost all of them tackled Mamdani fuzzy model, which was shown to have many interesting features. This paper aims to present alternatives for traditional inference mechanisms and CRI method. The most attractive advantage of these new m...

We describe the basics of fuzzy sets and fuzzy logic. Based upon the concept of linguistic values, which describe imprecise concepts using words, the basics of fuzzy rules and fuzzy inference are introduced. In the second part we briefly explain applications of fuzzy rules for function approximation using fuzzy graphs, clustering using fuzzy algorithms, and classification under uncertainty usin...

A multiobjective security game problem with fuzzy payoffs is studied in this paper. The problem is formulated as a bilevel programming problem with fuzzy coefficients. Using the idea of nearest interval approximation of fuzzy numbers, the problem is transformed into a bilevel programming problem with interval coefficients. The Karush-Kuhn-Tucker conditions is applied then to reduce the problem ...