Gotowe bibliografie tematyczne / Adaptive algorithm

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  2. Rozprawy doktorskie
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  4. Części książek
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Gotowa bibliografia na temat „Adaptive algorithm”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Adaptive algorithm"

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Litvinchuk, Yuliia. "Self-adaptive CMA-ES Algorithm." Mathematical and computer modelling. Series: Physical and mathematical sciences 24 (December 5, 2023): 81–90. http://dx.doi.org/10.32626/2308-5878.2023-24.81-90.

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This article will consider one of the self-adaptive algorithms for selecting parameters of complex systems, examples of which are neural networks. Self-adaptive algorithms are algorithms that change their behavior at runtime based on available information and predetermined reward mechanisms. These algorithms are widely used in various fields, including machine learning, optimization, and data compression. The self-adaptiveness of the algorithm in this case will be based on the selection of the number of peaks in the mixture of distributions in the extended CMA-ES algorithm under the condition
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Zhang, Zhaoxia. "Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm." International Journal of Intelligent Information Technologies 20, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijiit.344024.

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Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adapt
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Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao. "Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 590–97. http://dx.doi.org/10.1609/aaai.v34i01.5398.

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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive gr
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Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao. "Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization." Journal of Artificial Intelligence Research 74 (May 26, 2022): 303–51. http://dx.doi.org/10.1613/jair.1.12997.

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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive gr
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O'Malley, Lawrence V. "Adaptive clustering algorithm." IBM Journal of Research and Development 29, no. 1 (1985): 68–72. http://dx.doi.org/10.1147/rd.291.0068.

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Kusuma, Purba Daru, and Meta Kallista. "Adaptive Cone Algorithm." International Journal on Advanced Science, Engineering and Information Technology 13, no. 5 (2023): 1605. http://dx.doi.org/10.18517/ijaseit.13.5.18284.

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Guan, Sihai, Qing Cheng, Yong Zhao, and Bharat Biswal. "Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function." PLOS ONE 16, no. 10 (2021): e0258155. http://dx.doi.org/10.1371/journal.pone.0258155.

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Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More importantly, the hyperbolic sine function has not been discussed. In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function. Specifically, development of a robust adaptive filtering algorithm based o
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Sarma, Priyanka, and Atowar Ul Islam. "Implementation of Hybrid Adaptive Learning Algorithm for Task Offloading." Indian Journal Of Science And Technology 17, no. 38 (2024): 3929–36. http://dx.doi.org/10.17485/ijst/v17i38.2280.

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Objectives: Offloading tasks in edge computing (EC) plays an important role in optimizing resource utilization and enhancing the system performance. This paper studies various AI-based computation offloading (CO) strategies and proposes a hybrid Adaptive Learning Algorithm, that reduces the latency significantly compared to traditional RL-based on-policy and off-policy algorithms namely Q-Learning (QL) and State Action Reward State Action (SARSA) for CO in an EC environment. The paper evaluates and compares the efficiency of the proposed algorithms in optimizing dynamic offloading decisions, f
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Zhang, Qingyang, Tianji Peng, Guangchun Zhang, et al. "An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing." Science and Technology of Nuclear Installations 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/5549602.

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This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel a
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Xi, Zichen. "Analysis of Adaptive Equalization Algorithms." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 295–305. http://dx.doi.org/10.54097/hset.v70i.12477.

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Adaptive equalization algorithms play a pivotal role in suppressing inter-symbol interference in wireless channels. Contemporarily, with the rapid development of science and technology, there is still a lack of unified cognition for adaptive equalization algorithms. Therefore, this study systematically discusses the research status and development process of adaptive equalization algorithms, focusing on the least mean square algorithm (LMS), constant modulus blind equalization algorithm (CMA) and neural network algorithm. Subsequently, based on Matlab simulation, their performance is analyzed
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Rozprawy doktorskie na temat "Adaptive algorithm"

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Mirzazadeh, Mehdi. "Adaptive Comparison-Based Algorithms for Evaluating Set Queries." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1147.

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In this thesis we study a problem that arises in answering boolean queries submitted to a search engine. Usually a search engine stores the set of IDs of documents containing each word in a pre-computed sorted order and to evaluate a query like "computer AND science" the search engine has to evaluate the union of the sets of documents containing the words "computer" and "science". More complex queries will result in more complex set expressions. In this thesis we consider the problem of evaluation of a set expression with union and intersection as operators and ordered sets as operands.
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Law, Nga Lam. "Parameter-free adaptive genetic algorithm /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202007%20LAW.

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au, Daniel Schubert@csiro, and Daniel Schubert. "A Multivariate Adaptive Trimmed Likelihood Algorithm." Murdoch University, 2005. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20061019.132720.

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The research reported in this thesis describes a new algorithm which can be used to robustify statistical estimates adaptively. The algorithm does not require any pre-specified cut-off value between inlying and outlying regions and there is no presumption of any cluster configuration. This new algorithm adapts to any particular sample and may advise the trimming of a certain proportion of data considered extraneous or may divulge the structure of a multi-modal data set. Its adaptive quality also allows for the confirmation that uni-modal, multivariate normal data sets are outlier free. It is a
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Schubert, Daniel Dice. "A multivariate adaptive trimmed likelihood algorithm /." Access via Murdoch University Digital Theses Project, 2005. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20061019.132720.

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Vogt, Paul E. "An adaptive multi-scene correlation algorithm." Master's thesis, University of Central Florida, 1988. http://digital.library.ucf.edu/cdm/ref/collection/RTD/id/76421.

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University of Central Florida College of Engineering Thesis<br>Digital scene matching algorithms have been used in both military and commercial image processing systems for years. The trend toward using multiple sensors in military imaging systems has generated anew interest in real time techniques to accomplish sensor fusion tasks such as field of view alignment. This thesis analyzes methods presently in use and intorduces a novel algorithm that improves scene correlation performance. The focus of the new technique is in the segmentation area, where significant features are extracted from
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Turner, Steven Primitivo. "Adaptive out of step relay algorithm." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-01242009-063244/.

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Schubert, Daniel. "A multivariate adaptive trimmed likelihood algorithm." Thesis, Schubert, Daniel (2005) A multivariate adaptive trimmed likelihood algorithm. PhD thesis, Murdoch University, 2005. https://researchrepository.murdoch.edu.au/id/eprint/295/.

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The research reported in this thesis describes a new algorithm which can be used to robustify statistical estimates adaptively. The algorithm does not require any pre-specified cut-off value between inlying and outlying regions and there is no presumption of any cluster configuration. This new algorithm adapts to any particular sample and may advise the trimming of a certain proportion of data considered extraneous or may divulge the structure of a multi-modal data set. Its adaptive quality also allows for the confirmation that uni-modal, multivariate normal data sets are outlier free. It is a
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Schubert, Daniel. "A multivariate adaptive trimmed likelihood algorithm." Schubert, Daniel (2005) A multivariate adaptive trimmed likelihood algorithm. PhD thesis, Murdoch University, 2005. http://researchrepository.murdoch.edu.au/295/.

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The research reported in this thesis describes a new algorithm which can be used to robustify statistical estimates adaptively. The algorithm does not require any pre-specified cut-off value between inlying and outlying regions and there is no presumption of any cluster configuration. This new algorithm adapts to any particular sample and may advise the trimming of a certain proportion of data considered extraneous or may divulge the structure of a multi-modal data set. Its adaptive quality also allows for the confirmation that uni-modal, multivariate normal data sets are outlier free. It is a
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Doss, Christopher. "Algorithm Partitioning and Scheduling for Adaptive Computers." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010619-175307.

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<p>Adaptive, or reconfigurable, computing has emerged as a viable computing alternative for computationally intense applications. (We use the terms adaptive and reconfigurable interchangeably). Here, an adaptive computer is a computing system that contains a general purpose processor attached to a programmable logic device such as a field programmable gate array (FPGA). These computing systems combine the flexibility of general purpose processors with the speed of application specific processors. The computer system designer can cater the hardware to a specific application by modifying the con
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Pervaiz, Mehtab M. "Spatio-temporal adaptive algorithm for reacting flows." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/34994.

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Książki na temat "Adaptive algorithm"

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R, Hanebutte Ulf, and Langley Research Center, eds. A parallel adaptive mesh refinement algorithm. National Aeronautics and Space Administration, Langley Research Center, 1993.

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Birtles, B. The BRESTART self adaptive optimum start algorithm. Building Research Establishment, 1985.

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O'Toole, Gregory J. An investigation of the UTIAS adaptive washout algorithm. National Library of Canada, 1995.

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G, Hills Richard, and United States. National Aeronautics and Space Administration., eds. An adaptive grid algorithm for one-dimensional nonlinear equations. National Aeronautics and Space Administration, 1990.

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Shapiro, Richard A., ed. Adaptive Finite Element Solution Algorithm for the Euler Equations. Vieweg+Teubner Verlag, 1991. http://dx.doi.org/10.1007/978-3-322-87879-3.

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Ashby, Steven F. ChebyCode, a FORTRAN implementation of Manteuffel's adaptive Chebyshev algorithm. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1985.

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Shapiro, Richard A. Adaptive finite element solution algorithm for the Euler equations. Vieweg, 1991.

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Zaharia, Ramona. Adaptive compression algorithm for full colour three dimensional integral images. De Montfort University, 2001.

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United States. National Aeronautics and Space Administration., ed. The minimal time detection algorithm. National Aeronautics and Space Administration, 1995.

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Rogers, David. G/SPINES: A hybird of friedman's multivariate adaptive regression splines (MARS) algorithm with Holland's genetic algorithm. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1991.

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Części książek na temat "Adaptive algorithm"

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Tan, Ying. "Adaptive Fireworks Algorithm." In Fireworks Algorithm. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46353-6_8.

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Williams, Ross N. "The DHPC Algorithm." In Adaptive Data Compression. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5_2.

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Williams, Ross N. "A Multimodal Algorithm." In Adaptive Data Compression. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5_5.

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Williams, Ross N. "An Experimental Adaptive Algorithm." In Adaptive Data Compression. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5_4.

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Diniz, Paulo Sergio Ramirez. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_3.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29057-3_3.

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Diniz, Paulo S. R. "Quantization Effects in the LMS Algorithm." In Adaptive Filtering. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_15.

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Diniz, Paulo S. R. "Quantization Effects in the RLS Algorithm." In Adaptive Filtering. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_16.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_3.

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Diniz, Paulo S. R. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-68606-6_3.

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Streszczenia konferencji na temat "Adaptive algorithm"

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Trabia, Mohamed B., and Xiao Bin Lu. "A Fuzzy Adaptive Simplex Search Optimization Algorithm." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8586.

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Abstract Optimization algorithms usually use fixed parameters that are empirically chosen to reach the minimum for various objective functions. This paper shows how to incorporate fuzzy logic in optimization algorithms to make the search adaptive to various objective functions. This idea is applied to produce a new algorithm for minimization of a function of n variables using an adaptive form of the simplex method. The search starts by generating a simplex with n+1 vertices. The algorithm replaces the point with the highest function value by a new point. This process comprises reflecting the p
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Kanev, F. Yu, V. P. Lukin, and L. N. Lavrinova. "Dynamic Adaptive Mirror in the Algorithm of Phase Conjugation." In Adaptive Optics. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/adop.1995.tua52.

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Considering numerically the problem of adaptive compensation of intensive laser beam atmospheric aberrations we demonstrated the influence of transient processes that occur in adaptive systems on the effectiveness and rate of control.
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Feng, Xu, and Wenjian Yu. "A Fast Adaptive Randomized PCA Algorithm." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/411.

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It is desirable to adaptively determine the number of dimensions (rank) for PCA according to a given tolerance of low-rank approximation error. In this work, we aim to develop a fast algorithm solving this adaptive PCA problem. We propose to replace the QR factorization in randQB_EI algorithm with matrix multiplication and inversion of small matrices, and propose a new error indicator to incrementally evaluate approximation error in Frobenius norm. Combining the shifted power iteration technique for better accuracy, we finally build up an algorithm named farPCA. Experimental results show that
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Ellerbroek, Brent L., and Troy A. Rhoadarmer. "Optimization of Closed-Loop Adaptive-Optics Control Algorithms Using Measured Performance Data: Experimental Results." In Adaptive Optics. Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.athb.2.

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Experiments have shown the reward adaptive-optics provides in improving the resolution of ground-based astronomical telescopes [1,2,3]. A critical contributor to adaptive-optics system performance is the control algorithm that converts wavefront sensor (WFS) measurements into the deformable mirror (DM) actuator commands. For the adaptive-optics systems in use today this control algorithm consists of a wavefront reconstruction step to estimate the instantaneous phase distortion to be compensated [4], followed by a servo control law to temporally filter this instantaneous estimate before it is a
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Pruidze, D. V., J. C. Ricklin, D. G. Voelz, and M. A. Vorontsov. "Adaptive Correction of Phase-Distorted Extended Source Images: Experimental Results." In Adaptive Optics. Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.athb.3.

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We demonstrate a new type of adaptive imaging system capable of improving the quality of phase-distorted images of extended objects. The operational algorithm was based on optimization of the spectral image quality criteria suggested in [1]. For adaptive control of the nine-electrode semi-passive bimorph mirror we used gradient optimization algorithms. To introduce slowly varying large scale phase distortions into the imaging system a second deformable mirror with computer control was used. Small scale phase distortions were created using the nonlinear optics technique described in [2]. Image
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Yang, Peng, Peilin Zhao, and Xin Gao. "Bandit Online Learning on Graphs via Adaptive Optimization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/415.

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Traditional online learning on graphs adapts graph Laplacian into ridge regression, which may not guarantee reasonable accuracy when the data are adversarially generated. To solve this issue, we exploit an adaptive optimization framework for online classification on graphs. The derived model can achieve a min-max regret under an adversarial mechanism of data generation. To take advantage of the informative labels, we propose an adaptive large-margin update rule, which enjoys a lower regret than the algorithms using error-driven update rules. However, this algorithm assumes that the full inform
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Rhoadarmer, Troy A., and Brent L. Ellerbroek. "Optimization of Closed-Loop Adaptive Optics Wavefront Reconstruction Algorithms Using Experimentally Measured Performance Data: Experimental Results." In Adaptive Optics. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/adop.1995.fa7.

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Experiments have shown the reward adaptive optics provides in improving the resolution of ground-based astronomical telescopes.1-2 A critical contributor to adaptive optics system performance is the reconstruction algorithm that converts wavefront sensor (WFS) measurements into the deformable mirror (DM) actuator commands.3-4 Minimum variance reconstruction algorithms have been developed extensively to optimize the performance of adaptive optics systems given specific atmospheric conditions.5-7 These algorithms depend on atmospheric parameters which are seldom known exactly and are constantly
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Tran, Tan D., Canh V. Pham, Dung T. K. Ha, and Phuong N. H. Pham. "Improved Parallel Algorithm for Non-Monotone Submodular Maximization under Knapsack Constraint." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/217.

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This work proposes an efficient parallel algorithm for non-monotone submodular maximization under a knapsack constraint problem over the ground set of size n. Our algorithm improves the best approximation factor of the existing parallel one from 8 to 7 with O(log n) adaptive complexity. The key idea of our approach is to create an alternate threshold algorithmic framework. This new strategy alternately constructs two disjoint candidate solutions within a constant number of sequence rounds. Then, the algorithm boosts solution quality without sacrificing the adaptive complexity. Extensive experi
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Gyorodi, C., R. Gyorodi, M. Pater, O. Boc, and Z. David. "Adaptive AFOPT algorithm." In Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05). IEEE, 2005. http://dx.doi.org/10.1109/synasc.2005.17.

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Li, Junzhi, Shaoqiu Zheng, and Ying Tan. "Adaptive Fireworks Algorithm." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900418.

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Raporty organizacyjne na temat "Adaptive algorithm"

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Allen, Donald S., Yang-Woo Kim, and Meenakshi Pasupathy. Forecasting with an Adaptive Control Algorithm. Federal Reserve Bank of St. Louis, 1996. http://dx.doi.org/10.20955/wp.1996.009.

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Saltzman, J. S., D. L. Brown, K. D. Brislawn, G. S. Chesshire, D. J. Quinlan, and M. Berger. Adaptive mesh refinement algorithm development and dissemination. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/515637.

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Scannapieco, Anthony J. An Adaptive Mesh Algorithm: Mesh Structure and Generation. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1291247.

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Scannapieco, Anthony J. An Adaptive Mesh Algorithm: Mapping the Mesh Variables. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1304744.

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Chang, Hyeong S., Michael C. Fu, and Steven I. Marcus. An Adaptive Sampling Algorithm for Solving Markov Decision Processes. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada438505.

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Bhagdikar, Piyush, Stanislav Gankov, Jayant Sarlashkar, and Scott Hotz. Energy-Efficient Adaptive Cruise Control Using Eco-Driving Algorithm. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2569146.

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Torrieri, Don. The Anticipative Maximum Adaptive-Array Algorithm for Frequency-Hopping Systems. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada448152.

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Havens, Tim C., Dominic K. Ho, Justin Farrell, et al. Locally Adaptive Detection Algorithm for Forward-Looking Ground-Penetrating Radar. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada545174.

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Hatke, Gary F., and Stuart C. Schwartz. A Robust Adaptive Array Structure Using the Soft Constrained LMS algorithm. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203959.

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Kim, Dai H. A Highly Functional Decision Paradigm Based on Nonlinear Adaptive Genetic Algorithm. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada281457.

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