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In previous studies we first concentrated on utilizing crisp simulationto produce discrete event fuzzy systems simulations. Then we extendedthis research to the simulation of continuous fuzzy systems models. In this paperwe continue our study of continuous fuzzy systems using crisp continuoussimulation. Consider a crisp continuous system whose evolution depends ondifferential equations. Such a system contains a number of parameters thatmust be estimated. Usually point estimates are computed and used in themodel. However these point estimates typically have uncertainty associatedwith them. We propose to incorporate uncertainty by using fuzzy numbers asestimates of these unknown parameters. Fuzzy parameters convert the crispsystem into a fuzzy system. Trajectories describing the behavior of the systembecome fuzzy curves. We will employ crisp continuous simulation to estimatethese fuzzy trajectories. Three examples are discussed.

The concepts of free modules, projective modules, injective modules and the likeform an important area in module theory. The notion of free fuzzy modules was introducedby Muganda as an extension of free modules in the fuzzy context. Zahedi and Ameriintroduced the concept of projective and injective L-modules. In this paper we give analternate definition for projective L-modules. We prove that every free L-module is aprojective L-module. Also we prove that if μ∈L(P) is a projective L-module, and if0→η f→ ν g→ μ →0 is a short exact sequence of L-modules then η⊕ μ >ν.Further it is proved that if μ∈L(P) is a projective L-module then μ is a fuzzy direct summandof a free L-module.

In this paper, a certain new connectedness of L-fuzzy subsets inL-topological spaces is introduced and studied by means of preclosed sets. Itpreserves some fundamental properties of connected set in general topology.Especially the famous K. Fan’s Theorem holds.

In this note we first redefine the notion of a fuzzy hypervectorspace (see [1]) and then introduce some further concepts of fuzzy hypervectorspaces, such as fuzzy convex and balance fuzzy subsets in fuzzy hypervectorspaces over valued fields. Finally, we briefly discuss on the convex (balanced)hull of a given fuzzy set of a hypervector space.

In this note by considering a complete lattice L, we define thenotion of an L-Fuzzy hyperrelation on a given non-empty set X. Then wedefine the concepts of (POM)L-Fuzzy graph, hypergraph and subhypergroupand obtain some related results. In particular we construct the categories ofthe above mentioned notions, and give a (full and faithful) functor form thecategory of (POM)L-Fuzzy subhypergroups ((POM)L-Fuzzy graphs) into thecategory of (POM)L-Fuzzy hypergraphs. Also we show that for each finiteobjects in the category of (POM)L-Fuzzy graphs, the coproduct exists, andunder a suitable condition the product also exists.

This paper presents the basic concepts of stability in fuzzy linguistic models. Theauthors have proposed a criterion for BIBO stability analysis of fuzzy linguistic modelsassociated to linear time invariant systems [25]-[28]. This paper presents the basic concepts ofstability in the general nonlinear and linear systems. This stability analysis method is verifiedusing a benchmark system analysis.

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