Results 21 to 30 of about 15,999 (262)
Building Energy Consumption Prediction Using a Deep-Forest-Based DQN Method
Buildings, 2022 When deep reinforcement learning (DRL) methods are applied in energy consumption prediction, performance is usually improved at the cost of the increasing computation time.
Qiming Fu +5 more
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Some new lacunary statistical convergence with ideals
Journal of Inequalities and Applications, 2017 In this paper, the idea of lacunary I λ $I_{\lambda}$ -statistical convergent sequence spaces is discussed which is defined by a Musielak-Orlicz function.
Adem Kilicman, Stuti Borgohain
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An Improved Iterative Reweighted STAP Algorithm for Airborne Radar
Remote Sensing, 2022 In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples.
Weichen Cui +3 more
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Strong Statistical Convergence in Probabilistic Metric Spaces
Stochastic Analysis and Applications, 2008 In this article, we introduce the concepts of strongly statistically convergent sequence and strong statistically Cauchy sequence in a probabilistic metric (PM) space endowed with the strong topology, and establish some basic facts. Next, we define the strong statistical limit points and the strong statistical cluster points of a sequence in this space
Sencimen, C., Pehlivan, S.
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Ideal Convergence of Random Variables
Journal of Function Spaces and Applications, 2013 The aim of this paper is to introduce and study the notion of I-convergence of random variables via probabilistic norms. Furthermore, we introduce I-convergence in Lp space and establish some interesting results.
B. Hazarika, S. A. Mohiuddine
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Nonlinear Analysis, 2019 In this paper, we introduce the concept of enhanced probabilistic metric space (briefly EPM-space) as a type of probabilistic metric space. Also, we investigate the existence of fixed points for a (finite or infinite) linear combination of different ...
Jafari Jafari +3 more
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International Journal of Interactive Multimedia and Artificial Intelligence, 2022 Sampling-based motion planning in the field of robot motion planning has provided an effective approach to finding path for even high dimensional configuration space and with the motivation from the concepts of sampling based-motion planners, this paper
Lhilo Kenye, Rahul Kala
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Convergence in probability and almost surely convergence in probabilistic normed spaces [PDF]
Mathematical Sciences, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Beitollahi, Arman, Azhdari, Parvin
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On Some Further Generalizations of Strong Convergence in Probabilistic Metric Spaces Using Ideals
Abstract and Applied Analysis, 2013 Following the line of (Das et al., 2011, Savas and Das, 2011), we make a new approach in this paper to extend the notion of strong convergence and more general strong statistical convergence (Şençimen and Pehlivan, 2008) using ideals and introduce the ...
Pratulananda Das +3 more
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Some Results on Best Proximity Points of Cyclic Contractions in Probabilistic Metric Spaces
Journal of Function Spaces, 2015 This paper investigates properties of convergence of distances of p-cyclic contractions on the union of the p subsets of an abstract set X defining probabilistic metric spaces and Menger probabilistic metric spaces as well as the characterization of ...
Manuel De la Sen, Erdal Karapınar
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