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On Generalized Difference Rough Ideal Statistical Convergence in Neutrosophic Normed Spaces [PDF]
Neutrosophic Sets and SystemsThis article’s main goal is to provide and investigate a novel statistical convergence generalisation for generalized difference sequences in Neutrosophic Normed Spaces (NNS) called rough ideal statistical convergence.
Manpreet Kaur, Meenakshi Chawla
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PLoS ONE, 2023 Mortise and tenon joints are widely used in the building and furniture industries because of their excellent mechanical and eco-friendly properties. In real-life cases, there are usually many available alternative structures for a joint area, it is a ...
Bin Shang, Zhe Chen, Qing Ma, Yuhang Tan
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ON ROUGH $I^*$ AND $I^K$-CONVERGENCE OF SEQUENCES IN NORMED LINEAR SPACES [PDF]
, 2022 In this paper, we have introduced first the notion of rough $I^*$-convergence in a normed linear space as an extension work of rough $I$-convergence and then rough $I^K$-convergence in more general way.
Kumar Banerjee, Amar, Paul, Anirban
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Ramazan Sunar, Rough convergence in A-metric spaces
European Journal of Mathematics and Applications, 2023 The purpose of this study is to define rough convergence of sequences in an A-metric space, discuss its fundamental properties, and examine the relationships between rough convergence and rough Cauchy sequence in A-metric spaces.
Mukaddes Arslan
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On deferred I-statistical rough convergence of difference sequences in neutrosophic normed spaces [PDF]
Neutrosophic Sets and SystemsIn this study, using the concepts of deferred density and the notion of the ideal I, we extend the idea of rough convergence by introducing the notion of deferred I–statistical rough convergence via difference operators in the framework of neutrosophic ...
Mukhtar Ahmad +2 more
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A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers [PDF]
Control and Optimization in Applied Mathematics, 2018 o enhance the performances of rough-neural networks (R-NNs) in the system identification, on the base of emotional learning, a new stable learning algorithm is developed for them.
Ghasem Ahmadi +2 more
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Modeling of Autonomous Underwater Vehicles with Multi-Propellers Based on Maximum Likelihood Method
Journal of Marine Science and Engineering, 2020 The hydrodynamic characteristics of multi-propeller autonomous underwater vehicles (AUV) is usually complicated and it is difficult to obtain an accurate mathematical model. A modeling method based on CFD calculation and maximum likelihood identification
Feiyan Min, Guoliang Pan, Xuefeng Xu
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ROUGH STATISTICAL CONVERGENCE OF COMPLEX UNCERTAIN TRIPLE SEQUENCE [PDF]
, 2022 We investigate the rough statistical convergence of complex uncertain triple sequences in this research. We show three forms of rough statistically convergent complex uncertain triple sequences and rough lambda(3)-statistical convergence in measure, as ...
Gurdal, M., Kisi, O.
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Markovian Approximation of the Rough Bergomi Model for Monte Carlo Option Pricing
Mathematics, 2021 The recently developed rough Bergomi (rBergomi) model is a rough fractional stochastic volatility (RFSV) model which can generate a more realistic term structure of at-the-money volatility skews compared with other RFSV models.
Qinwen Zhu +3 more
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Rough I2 ${I}_{2}$-lacunary statistical convergence of double sequences
Journal of Inequalities and Applications, 2018 In this paper, we introduce and study the notion of rough I2 $\mathcal {I}_{2}$-lacunary statistical convergence of double sequences in normed linear spaces.
Ömer Kişi, Erdinç Dündar
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