Results 11 to 20 of about 3,482,041 (300)
Lacunary weak statistical convergence [PDF]
Mathematica Bohemica, 2011 Summary: The aim of this work is to generalize lacunary statistical convergence to weak lacunary statistical convergence and \(\mathcal {I}\)-convergence to weak \(\mathcal {I}\)-convergence. We start by defining weak lacunary statistically convergent and weak lacunary Cauchy sequences. We find a connection between weak lacunary statistical convergence
Nuray, Fatih
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Generalized Statistical Convergence and Convergence Free Spaces
Journal of Mathematical Analysis and Applications, 2000 The statistical convergence was introduced by H. Fast in 1951. Since then this notion has been studied by many authors including J. Connor, J. A. Fridy, H. I. Miller, A. R. Freedman, J. J. Sember, I. J. Maddox, T. Salat, etc. In this paper, by using the concepts of convergence free spaces, cofilters and filters based on a countably infinite set, the ...
Nuray, F, Ruckle, WH
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On Mλm,n-Statistical Convergence
Journal of Mathematics, 2020 In this paper, we introduce a new type of statistical convergence method for double sequences by using the M,λm,n-method of summability which was defined by Natarajan.
Nazlım Deniz Aral, Şerife Günal
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A-Statistical Convergence [PDF]
, 2016 The present study is conducted to study a new type convergence, called A-statistical convergence. In the beginning of the study, the concept of infinite, non-negative regular matrices is introduced. Some basic properties of regular and conservative matrices are studied.
Yousuf, Saadia
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Predicting Future Promising Technologies Using LSTM
Informatics, 2022 With advances in science and technology and changes in industry, research on promising future technologies has emerged as important. Furthermore, with the advent of a ubiquitous and smart environment, governments and enterprises are required to predict ...
Seol-Hyun Noh
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Analysis of Gradient Vanishing of RNNs and Performance Comparison
Information, 2021 A recurrent neural network (RNN) combines variable-length input data with a hidden state that depends on previous time steps to generate output data. RNNs have been widely used in time-series data analysis, and various RNN algorithms have been proposed ...
Seol-Hyun Noh
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Performance Comparison of CNN Models Using Gradient Flow Analysis
Informatics, 2021 Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing.
Seol-Hyun Noh
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On (f, ρ)−Statistical convergence and strong (f, ρ)−summability of order α [PDF]
E-Journal of Analysis and Applied Mathematics, 2022 The main object of this article is to introduce the concepts of (f, ρ)− statistical convergence of order α and strong (f, ρ)− summability of order α of sequences of real numbers and give some inclusion relations between these spaces.
Hacer Şengül Kandemir +2 more
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Lacunary statistical convergence [PDF]
Pacific Journal of Mathematics, 1993 The sequence \(x\) is statistically convergent to \(L\) provided that for each \(\varepsilon>0\), \[ \lim_ n {1 \over n} \{\text{the number of } k \leq n:| x_ k-L | \geq \varepsilon\}=0. \] A related concept is introduced by replacing the set \(\{k:k \leq n\}\) with \(\{ k:k_{r-1}
Fridy, J. A., Orhan, C.
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ON DISCRETE WEIGHTED STATISTICAL CONVERGENCE [PDF]
Facta Universitatis, Series: Mathematics and Informatics, 2021 In the present paper, the notion of discrete weighted mean method of summability isextended the concept of statistical convergence. We also give the notion of statistical (M,P_{λ})-summability and [M,P_{λ}]_{q}-summability.
Ercan, Sinan +2 more
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