Raheleh Jafari

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Many uncertain nonlinear systems can be modeled by the linear-in-parameter model, and the parameters are uncertain in the sense of fuzzy numbers. Fuzzy equations can be used to model these nonlinear systems. The solutions of the fuzzy equations are the controllers. In this paper, we give the controllability condition for the fuzzy control via dual fuzzy(More)
Many uncertain nonlinear systems can be modeled by linear-in-parameter models. The uncertainties can be regarded as parameter changes, which can be described as fuzzy numbers. These models are fuzzy equations. They are alternative models for uncertain nonlinear systems. The modeling of the uncertain nonlinear systems is to find the coefficients of the fuzzy(More)
Partial differential equations (PDEs) emerges in modeling innumerable phenomena that applies in science and technology. In this current work, a methodology involving novel iterative technique considering feed-forward neural networks (FNNs) is suggested to extract approximate solution for the second-order nonlinear PDEs with real constant coefficients (RCCs)(More)
Let (R, m) be a complete Noetherian local ring and let M be a finite R–module of positive Krull dimension n. It is shown that any subset T of AsshR(M) can be expressed as the set of attached primes of the top local cohomology module H na (M) for some ideal a of R. Moreover if a is an ideal of R such that the set of attached primes of H na (M) is a non–empty(More)
This paper highlights the concepts related to the fuzzy Sumudu transform (FST). Few important theorems are illustrated for uncovering the properties of FST. By utilizing the generalized FST, the fuzzy differential equations (FDEs) are resolved. The suggested technique is validated by laying down two real examples.