Journal of Mahani Mathematical Research
Volume:7 Issue: 2, Summer and Autumn 2018

Taking into account the notion of BL-general fuzzy automaton, in the present study we define the notation of BL-intuitionistic general L-fuzzy automaton and I-bisimulation for BL-intuitionistic general L-fuzzy automaton.Then for a given BL-intuitionistic general L-fuzzy automaton, we obtain the greatest I-bisimulation. According to this notion, we give the structure of quotient BL-intuitionistic general L-fuzzy automaton. Fortunately, this quotient is the minimal BL-intuitionistic general L-fuzzy automaton. In addition, in this study, we show that if there is an I-bisimulation between two BL-intuitionistic general L-fuzzy automata, then they have the same behavior. Furthermore, we give an algorithm which determines the I-bisimulation between any two BL-intuitionistic general L-fuzzy automata. To clarify the notions and the results obtained in this paper, we have submitted some examples as well.

ABSTRACT. Let R be a commutative noetherian ring, I and J are two ideals of R. Inthis paper we introduce the concept of (I;J)- minimax R- module, and it is shown thatif M is an (I;J)- minimax R- module and t a non-negative integer such that HiI;J(M) is(I;J)- minimax for all i

A matrix R is said to be g-row substochastic if Re ≤ e. For X, Y ∈ Mn,m, it is said that X is sglt-majorized by Y , X ≺sglt Y , if there exists an n-by-n lower triangular g-row substochastic matrix R such that X = RY . This paper characterizes all (strong) linear preservers and strong linear preservers of ≺sglt on Rn and Mn,m, respectively.

‎In this article‎, ‎an efficient numerical technique for solving the two-dimensional time-dependent Schrodinger equation is presented‎. ‎At first‎, ‎we employ the meshless‎‎local Petrov-Galerkin (MLPG) method based on a local weak formulation to construct a system of discretized equations and then the solution of time-dependentSchrodinger‎equation will be approximated‎. ‎We use the Moving Kriging (MK) interpolation instead‎‎of Moving least Square (MLS) approximation to construct the MLPG shape functions‎‎and hence the Heaviside step function is chosen to be the test function‎. ‎In this method‎,‎no mesh is needed neither for integration of the local weak form nor construction of the‎‎shape functions‎. ‎So‎, ‎the MLPG is truly a meshless method‎. ‎Several numerical examples‎‎are presented and the results are compared to their analytical and RBF‎‎solutions to illustrate the accuracy and capability of this algorithm‎.