We extend Knuth's 16 Boolean binary logic operators to fuzzy logic and neutrosophic logic binary operators. Then we generalize them to n-ary fuzzy logic and neutrosophic logic operators using the smarandache codification of the Venn diagram and a defined vector neutrosophic law. In such way, new operators in neutrosophic logic/set/probability are built. Introduction. For the beginning let’s con...

In this paper, we present optimal control for movement and trajectory planning for four degrees-of-freedom robot using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs) for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like; Movement, Friction and Settling Time in robotic arm movement have been compensated using Fuz...

Applications of fuzzy logic (FL) to power electronics and drives are on the rise. The paper discusses some representative applications of FL in the area, preceded by an interpretative review of fuzzy logic controller (FLC) theory. A discussion on design and implementation aspects is presented, that also considers the interaction of neural networks and fuzzy logic techniques. Finally, strengths ...

Since Zadeh [21] introduced Fuzzy Sets, many discussions have taken place whether Fuzzy Logic (FL) deserves a place in control theory. Three properties speak in favour of FL control. The first being its robustness against parameter uncertainty [3] [12], the second the fact that the FL controller output is normalized. While linear PID controllers assume that the controller output can be much lar...

In this research paper we have presented control architecture for robotic arm movement and trajectory planning using Fuzzy Logic (FL) and Genetic Algorithms (GAs). This architecture is used to compensate the uncertainties like; movement, friction and settling time in robotic arm movement. The genetic algorithms and fuzzy logic is used to meet the objective of optimal control movement of robotic...

The paper presents the potential of fuzzy logic (FL-I) and neural network techniques (ANN-I) for predicting the compressive strength, for SCC mixtures. Six input parameters that is contents of cement, sand, coarse aggregate, fly ash, superplasticizer percentage and water-to-binder ratio and an output parameter i.e. 28day compressive strength for ANN-I and FL-I are used for modeling. The fuzzy l...

Fuzzy Logic (FL) and fuzzy sets in a wide interpretation of FL (in terms in which fuzzy logic is coextensive with the theory of fuzzy sets, that is, classes of objects in which the transition from membership to non membership is gradual rather than abrupt) have placed modelling into a new and broader perspective by providing innovative tools to cope with complex and ill-defined systems. The are...

In this work a novel Multi-Sensor Data Fusion (MSDF) architecture is presented. First, each measurement-vector coming from each sensor is fed to a Fuzzy Logic-based Adaptive Kalman Filter (FL-AKF); thus there are N sensors and N FL-AKFs working in parallel. The adaptation in each FL-AKF is in the sense of dynamically tuning the measurement noise covariance matrix R employing a fuzzy inference s...