In and we introduced a rst model of fuzzy projective geometries deduced from respectively fuzzy vector spaces and fuzzy groups This provided a link between the fuzzy versions of classical theories that are very closely related However the geometric structure involved in this model is rather weak a fuzzy projec tive space in this sense is equivalent with a given se quence of subspaces in the bas...

Fuzzy logic is recently getting increasing emphasis in soft computing applications in the recent days. This paper presents an application of the fuzzy logic scheme to control the speed of an AC servo system & the same is proposed for a space vector pulse width modulation (SVPWM) inverter-fed permanent magnet synchronous motor (PMSM) drive. Based on the analysis of the PMSM transient response, a...

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of i...

Dimensional analysis, for multi-criteria decision making, is a mathematical method that includes diverse heterogeneous criteria into a single dimensionless index. Dimensional Analysis, in its current definition, presents the drawback to manipulate fuzzy information commonly presented in a multi-criteria decision making problem. To overcome such limitation, we propose two dimensional analysis ba...

in this paper, we have dened and studied a generalized form of topological vectorspaces called s-topological vector spaces. s-topological vector spaces are dened by using semi-open sets and semi-continuity in the sense of levine. along with other results, it is provedthat every s-topological vector space is generalized homogeneous space. every open subspaceof an s-topological vector space is ...

We present an efficient method for extracting fuzzy classification rules from high dimensional data. A cluster estimation method called subtractive clustering is used to efficiently extract rules from a high dimensional feature space. A complementary search method can quickly identify the important input features from the resultant high dimensional fuzzy classifier, and thus provides the abilit...