This special issue of Journal of Computational Science follows the Agent-Based Simulations, adaptive algorithms and Solvers (ABS-AAS) Workshop in frame of the International Conference on Computational Science (ICCS) h...
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This special issue of Journal of Computational Science follows the Agent-Based Simulations, adaptive algorithms and Solvers (ABS-AAS) Workshop in frame of the International Conference on Computational Science (ICCS) held in Reykjavik, Iceland, in June 1-3, 2015. The aim of this workshop was to integrate results of different domains of computer science, computational science and mathematics. Chairmans of the ABS-AAS Worksop invited papers oriented toward simulations, either hard simulations by means of finite element or finite difference methods, or soft simulations by means of agent-based systems, evolutionary computations, and other. This was thirteen ABS-AAS workshop in frame of the ICCS conference. The workshop was organized by five co-chairmens, including Maciej Paszynski, Robert Schaefer and Krzysztof Cetnarowicz from AGH University, Krakow, Poland, David Pardo from the University of the Basque Country, Bilbao, Spain and Victor Calo from King Abdullah University of Science and Technology. (C) 2015 Elsevier B.V. All rights reserved.
The design of envelope-constrained filters is formulated as a constrained optimization problem. In this paper the constraint approximation is realized through a cubic natural spline, which results in an unconstrained ...
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The design of envelope-constrained filters is formulated as a constrained optimization problem. In this paper the constraint approximation is realized through a cubic natural spline, which results in an unconstrained optimization problem for envelope-constrained filter design. The solution of this unconstrained problem is suitable for real-time update. It is shown that, compared with the previously used quadratic natural spline, the cubic natural spline leads to the establishment of the adaptive algorithms with much move desirable performance. In particular, the step size of the cubic constraint approximation based adaptive algorithms can be chosen in a more flexible manner so as to achieve faster convergence. Numerical examples illustrate the main results.
The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms. In this brief, we generalize the application of th...
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The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms. In this brief, we generalize the application of the DCD algorithm to RLS adaptive filtering in impulsive noise scenarios and derive a unified update formula. By employing different robust strategies against impulsive noise, we develop novel computationally efficient DCD-based robust recursive algorithms. Furthermore, to equip the proposed algorithms with the ability to track abrupt changes in unknown systems, a simple variable forgetting factor mechanism is also developed. Simulation results for channel identification scenarios in impulsive noise demonstrate the effectiveness of the proposed algorithms.
In this article, we study split common fixed point problems with multiple output sets in real Hilbert spaces. In order to solve this problem, we present three new self-adaptive algorithms. We establish weak and strong...
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Compared with traditional computer network security identification techniques, deep learning algorithms are based on their own distributed network structure for storage, processing, classification, comparison, and aut...
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In this paper, fixed point problems of pseudocontractive operators are investigated. We present a self-adaptive algorithm for finding a fixed point of a Lipschitz pseudocontractive operator in a real Hilbert space. Ou...
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This is an overview of recent results on complexity and optimality of adaptive algorithms for integrating and approximating scalar piecewise r-smooth functions with unknown singular points. We provide adaptive algorit...
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This is an overview of recent results on complexity and optimality of adaptive algorithms for integrating and approximating scalar piecewise r-smooth functions with unknown singular points. We provide adaptive algorithms that use at most n function samples and have the worst case errors proportional to n(-r) for functions with at most one unknown singularity. This is a tremendous improvement over nonadaptive algorithms whose worst case errors are at best proportional to n(-1) for integration and n(-1/p) for the L-p approximation problem. For functions with multiple singular points the adaptive algorithms cease to dominate the nonadaptive ones in the worst case setting. Fortunately, they regain their superiority in the asymptotic setting. Indeed, they yield convergence of order n(-r) for piecewise r-smooth functions with an arbitrary (unknown but finite) number of singularities. None of these results hold for the L-a approximation. However, they hold for the Skorohodmetric, which we argue to be more appropriate than L-a for dealing with discontinuous functions. Numerical test results and possible extensions are also discussed.
The paper deals with extraction of fetal phonocardiogram (fPCG) from the abdominal signal mixture by using adaptive filters based on Least Mean Squares (LMS) algorithms. Optimal setting of the filter parameters is a v...
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This paper focuses on C (0)IPG adaptive algorithms for the biharmonic eigenvalue problem with the clamped boundary condition. We prove the reliability and efficiency of the a posteriori error indicator of the approxim...
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This paper focuses on C (0)IPG adaptive algorithms for the biharmonic eigenvalue problem with the clamped boundary condition. We prove the reliability and efficiency of the a posteriori error indicator of the approximating eigenfunctions and analyze the reliability of the a posteriori error indicator of the approximating eigenvalues. We present two adaptive algorithms, and numerical experiments indicate that both algorithms are efficient.
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