This paper focuses on the Fuzzy Logic System for fetal heart rate determination. The clinical interpretation of fetal heart rate trace is a difficult task and this has led to the development of computerised systems. These systems are limited by their inability to represent uncertainty. This paper describes the development of a fuzzy expert system for fetal heart rate. The fuzzy logic system imp...

In this paper, we present a formal derivation of general nonsingleton fuzzy logic systems (NSFLS’s) and show how they can be efficiently computed. We give examples for special cases of membership functions and inference and we show how an NSFLS can be expressed as a “nonsingleton fuzzy basis function” expansion and present an analytical comparison of the nonsingleton and singleton fuzzy logic s...

1. Formulation of the problem 1:1: It is necessary to represent implication in expert systems Our knowledge of complex systems is often incomplete, and therefore, we have to rely on the expert's statements, These statements are usually formulated not in mathematical terms, but in words of natural languages. Fuzzy logic is a methodology for formalizing such statements. Since a large part of expe...

Fuzzy control is based on fuzzy logic, which provides a most efficient way to handle inexact information as a basis of reasoning. With fuzzy logic it is possible to convert knowledge, which is expressed in an uncertain form, to an exact and precise output for uncertain systems. In fuzzy control, the controller can be simply represented by if-then-else rules. The interpretation of the controller...

Interval type-2 fuzzy logic systems have a wide range of applications due to their abilities to handle uncertainties compared to their type-1 counterparts. This paper presents an approach for developing closed-form mathematical representations for interval type-2 fuzzy logic systems. Having such a representation can be useful in real-time implementation of the system and in its mathematical ana...

Interval Type-2 Fuzzy Logic Controller (IT2FLC) where the fuzzy parameters, e.g. Fuzzy Membership Functions and Fuzzy Rule Bases are tuned by Genetic Algorithm (GAs) known as Genetic Interval Type-2 Fuzzy System (GIT2FS). The GFS are discussed briefly, later, the proposed design is explained. Key wordsGA, Interval Type-2 Fuzzy Logic Controller (IT2FLC), Genetic Interval Type2 Fuzzy System (GIT2...

The task of scheduling in flexible manufacturing systems (FMS) is more complex and problematic than a traditional manufacturing systems. To accomplish great performance for FMS, a good scheduling system should make an accurate decision at an accurate time according to system situations. Fuzzy logic methodologies easily deal with indeterminate and incomplete information. Human expert‟s knowledge...

In this paper, we will consider the application of notions and techniques from fuzzy logic, fault trees, and Markov modeling to robot fault tolerance. Fuzzy logic lends itself to quantitative reliability calculations in robotics. The crisp failure rates which are usually used are not actually known, while fuzzy logic, due to its ability to work with the actual approximate (fuzzy) failure rates ...

Type-2 fuzzy logic to make up for the lack of a type of fuzzy logic in dealing with uncertainty, object contains uncertainty is strong; the application of type-2 fuzzy logic advantage is more obvious. In this paper, type-2s of fuzzy logic for power load time series forecasting, good results were obtained. According to the power load has strong randomness it is difficult to accurately forecast p...