Results 281 to 290 of about 7,666,446 (331)

A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

Journal of nonlinear science, 2014
The Koopman operator is a linear but infinite-dimensional operator that governs the evolution of scalar observables defined on the state space of an autonomous dynamical system and is a powerful tool for the analysis and decomposition of nonlinear ...
Matthew O. Williams, I. Kevrekidis, Clarence W. Rowley   +2 more
semanticscholar   +1 more source

Approximation Space and LEM2-like Algorithms for Computing Local Coverings

Fundamenta Informaticae, 2008
In this paper we discuss approximation spaces that are useful for studying local lower and upper approximations. Set definability and properties of the approximation space, including best approximations, are considered as well.
J. Grzymala-Busse, W. Rzasa
semanticscholar   +1 more source

Rough Approximations in General Approximation Spaces

2011
This paper is devoted to the discussion of rough approximations in general approximation space. The notions of transitive and Euclidean uncertainty mapping were introduced. The properties of some rough approximations were derived based on transitive and Euclidean uncertainty mappings.
Keyun Qin, Zheng Pei, Yang Xu
openaire   +1 more source

L -fuzzy approximating spaces

Journal of Intelligent & Fuzzy Systems, 2019
Rough set theory (for short, RST) are widely applied to artificial intelligence. Fuzzy rough sets (for short, FRSs) are the results of approximation of fuzzy sets on a fuzzy approximation space. In this paper, L -fuzzy is briefly denoted by LF . We study a topological problem of FRSs based on
Zeng, Jiasheng, Wang, Pei
openaire   +1 more source

Constrained Approximation in Banach Spaces

Constructive Approximation, 2003
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Optimized Tensor-Product Approximation Spaces

Constructive Approximation, 2000
The authors deal with the construction of finite element spaces for the approximate solution of symmetric elliptic variational problems in Sobolev spaces. They construct operator adapted finite element subspaces with a lower dimension than the standard full-grid spaces.
Griebel, M., Knapek, S.
openaire   +2 more sources

Compound Approximation Spaces

2017
This chapter develops compound approximation spaces and their constrained versions defined for relational data. These spaces are constructed based on information systems and the tolerance rough set model extended to a relational case. The chapter also shows that in constrained compound approximations spaces it is possible to approximate not only a ...
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Reinforcement Learning with Approximation Spaces

Fundamenta Informaticae, 2006
This paper introduces a rough set approach to reinforcement learning by swarms of cooperating agents. The problem considered in this paper is how to guide reinforcement learning based on knowledge of acceptable behavior patterns. This is made possible by considering behavior patterns of swarms in the context of approximation spaces.
Peters, James F., Henry, Christopher
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S-Approximation Spaces

2018
In this paper, the concept of S-approximation spaces is surveyed at first and then, the combination of different S-approximation spaces with different decider mappings S is considered, i.e. combining S-approximation spaces Gi = (Ui, Wi, Ti, Si) for i = 1, …, k.
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State-space approximation

1991
Abstract The emphasis in the previous chapters has been on assessing the reliability of a binary system, whose components (nodes and/or edges) can be in either of two states (operating or failed). However, reliability is only one of a number of stochastic performance measures that might be of practical interest.
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