Logic rules and membership functions are two key components of a fuzzy logic controller (FLC). If only one component is learned, the other one is often set subjectively thus can reduce the applicability of FLC. If both components are learned simultaneously, a very long chromosome is often needed thus may deteriorate the learning performance. To avoid these shortcomings, this paper employs genet...

The shapes of terms used in fuzzy systems have adopted several ‘conventions’. Terms are almost invariably normalised (having a maximum membership value of 1), convex (having a single maximum or plateau maxima) and distinct (being restricted in their degree of overlap: often expressed as some variation on the concept that all membership values at any point in the universe of discourse sum to 1 a...

Interval-valued fuzzy sets were proposed thirty years ago as a natural extension of fuzzy sets. Many variants of these mathematical objects exist, under various names. One popular variant proposed by Atanassov starts by the specification of membership and non-membership functions. This paper focuses on interpretations of such extensions of fuzzy sets, whereby the two membership functions that d...

| In this paper we discuss a fuzzy classi er with pyramidal membership functions and discuss a performance improvement by changing linear membership functions to quadratic membership functions. First we de ne the classi er with quadratic membership functions and then discuss the training method that maximizes the recognition rate by counting the net increase in the recognition rate by changing ...

One of the main challenges for transportation engineers is the consideration of pedestrian safety as the most vulnerable aspect of the transport system. In many countries around the world, a large number of accidents recorded by the police are composed of accidents involving pedestrians and vehicles, for example when pedestrians may be struck by passing vehicles when crossing the street. Carefu...

Development of fuzzy ifthen rules and formation of membership functions are the important consideration in designing a fuzzy classifier system. This paper presents a Modified Genetic Algorithm (ModGA) approach to obtain the optimal rule set and the membership function for a fuzzy classifier. In the genetic population, the membership functions are represented using real numbers and the rule set ...