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Artykuły w czasopismach na temat "Optimisation probabiliste"
Martinet, P., L. Lanfranco, D. Tandé, L. Picard, P. Danneels, S. Jamard, B. Gaborit, C. Danthu, C. Loheac i S. Rézig. "Pyélonéphrite aiguë du greffon : vers une optimisation de l'antibiothérapie probabiliste". Médecine et Maladies Infectieuses Formation 1, nr 2 (czerwiec 2022): S9. http://dx.doi.org/10.1016/j.mmifmc.2022.03.044.
Pełny tekst źródłaPitner, P., H. Procaccia, T. Riffard, B. Granger i B. Flesch. "Optimisation du contrôle et de la maintenance des faisceaux tubulaires des générateurs de vapeur grâce à l'analyse probabiliste". Revue Générale Nucléaire, nr 3 (maj 1993): 187–94. http://dx.doi.org/10.1051/rgn/19933187.
Pełny tekst źródłaCarrié, Cédric, Noémie Sauvage i Matthieu Biais. "Optimisation du traitement par β-Lactamines chez le patient de réanimation en hyperclairance rénale". Médecine Intensive Réanimation 30, nr 2 (18.05.2021): 157–64. http://dx.doi.org/10.37051/mir-00059.
Pełny tekst źródłaPasalodos-Tato, María, Timo Pukkala i Alberto Rojo Alboreca. "Optimal management of Pinus pinaster in Galicia (Spain) under risk of fire". International Journal of Wildland Fire 19, nr 7 (2010): 937. http://dx.doi.org/10.1071/wf08150.
Pełny tekst źródłaTayal, Shilpy. "Analysis of Information Geometry for Optimization and Inference Applications". Mathematical Statistician and Engineering Applications 70, nr 1 (31.01.2021): 621–27. http://dx.doi.org/10.17762/msea.v70i1.2516.
Pełny tekst źródłaLypchuk, Vasyl, i Vasyl Dmytriv. "Management of technological process optimisation". Engineering Management in Production and Services 12, nr 3 (1.09.2020): 103–15. http://dx.doi.org/10.2478/emj-2020-0022.
Pełny tekst źródłaShariatmadar, Keivan, i Mark Versteyhe. "Numerical Linear Programming under Non-Probabilistic Uncertainty Models — Interval and Fuzzy Sets". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, nr 03 (21.05.2020): 469–95. http://dx.doi.org/10.1142/s0218488520500191.
Pełny tekst źródłaVan Nguyen, N., J. W. Lee, Y. D. Lee i H. U. Park. "A multidisciplinary robust optimisation framework for UAV conceptual design". Aeronautical Journal 118, nr 1200 (luty 2014): 123–42. http://dx.doi.org/10.1017/s0001924000009027.
Pełny tekst źródłaHolický, Milan. "Fuzzy probabilistic optimisation of building performance". Automation in Construction 8, nr 4 (kwiecień 1999): 437–43. http://dx.doi.org/10.1016/s0926-5805(98)00090-9.
Pełny tekst źródłaal-Rifaie, Mohammad Majid, i Tim Blackwell. "Cognitive Bare Bones Particle Swarm Optimisation with Jumps". International Journal of Swarm Intelligence Research 7, nr 1 (styczeń 2016): 1–31. http://dx.doi.org/10.4018/ijsir.2016010101.
Pełny tekst źródłaRozprawy doktorskie na temat "Optimisation probabiliste"
Scherrer, Bruno. "Application et optimisation de l'échantillonnage probabiliste en écologie continentale". Montpellier 2, 1987. http://www.theses.fr/1987MON20115.
Pełny tekst źródłaBouillard, Anne. "Optimisation et analyse probabiliste de systèmes à évènements discrets". Lyon, École normale supérieure (sciences), 2005. http://www.theses.fr/2005ENSL0337.
Pełny tekst źródłaThis thesis deals with the study of discrete event systems. Three different models are considered. In the first part, we are interested in the trace groups. After giving a simple Möbius-like formula for the generating series of the trace groups, we show the existence and the algebraicity of the asymptotic growth rate of the height of the traces. The second part is devoted to timed free-choice nets, an important sub-class of Petri nets. We define the notion of throughput in those nets and study the variation of the throughput in function of the conflict resolution policies. First, we show how to compute the throughput, then we are interested in the policy that maximizes or minimizes the throughput. Finally, we give an efficient method to generate a marking according to its exact distribution in order to numerically evaluate the throughput. In the last part, we study the computation of performance guarantees in networks thanks to Network Calculus techniques. We show the stability of the ultimately pseudo-periodic functions with the operations of the Network Calculus and give algorithms to compute these functions. These techniques are then applied to the study of performance guarantees in graphs with turn prohibition
Scherrer, Bruno. "Application et optimisation de l'échantillonnage probabiliste en écologie continentale". Grenoble 2 : ANRT, 1987. http://catalogue.bnf.fr/ark:/12148/cb37609751c.
Pełny tekst źródłaSchmitt, Lucie. "Durabilité des ouvrages en béton soumis à la corrosion : optimisation par une approche probabiliste". Thesis, Toulouse, INSA, 2019. http://www.theses.fr/2019ISAT0009/document.
Pełny tekst źródłaMastering the durability of new structures and the need to extand the lifespan of existing constructions correspond to social issues of the highest order and are part of the principles of a circular economy. The durability of concrete structures thus occupies a central position in the normative context. This thesis works follow those of J. Mai-Nhu* and aims at extending the field of application the SDReaM-crete model by integrating mineral additions based concretes and by defining a limit state criterion based on a quantity of corroded products. An approach based on a numerical optimization of predictive computations is set up to perform reliability analyses by considering the main mechanisms related to the corrosion of reinforcement, carbonation and chlorides. This model enables the optimization of the sizing of the concrete covers and performances by further integrating the environmental conditions as defined by the standards
Bérard, Jean. "Contributions à l'étude probabiliste des algorithmes d'évolution". Lyon 1, 2001. http://www.theses.fr/2001LYO10223.
Pełny tekst źródłaBelkora, Samir. "Les méthodes d'optimisation multiobjectif : synthèse et considérations théoriques : application d'un modèle probabiliste de choix au problème d'optimisation multiobjectif". Aix-Marseille 2, 1986. http://www.theses.fr/1986AIX24012.
Pełny tekst źródłaThis work treat of multiple objective optimization methods. In a first large part, we accomplished the multiple objective methods synthesis by following a traditional classification which separate the methods in three major class : - the methods which require "a priori" ponderation of the objectives. - the methods which require "progressive" ponderation of the objectives or the interactive methods. - the methods which lead to "a posteriori" ponderation of the objectives. In a second part, we try to provide a theorical contribution to improve a method which belongs to the interactive methods class by integrating a qualitative choice probabilistic model which authorize sufficiently versatile specifications, the conditional probit model
Souissi, Salma. "Problème du Bin Packing probabiliste à une dimension". Versailles-St Quentin en Yvelines, 2006. http://www.theses.fr/2006VERS0052.
Pełny tekst źródłaIn the Probabilistic Bin Packing Problem (PBPP) the random deletion of some items once placed into bins. The problem is to rearrange the residual items, using the a priori solution. The initial arrangement being done with the Next Fit Decreasing Heuristic (NFD). We propose two resolution methodologies: the redistribution strategy according to NFD and the a priori strategy. In the first one, the Next fit algorithm is applied to the new list. In the second one, successive groups of bins are optimally rearranged. In both cases, we develop an average case analysis for the (PBPP). We prove the law of large numbers and the central limit theorem for the number of occupied bins as the initial number of items tends to infinity. We verify these theoretical results by simulation
Bahloul, Khaled. "Optimisation combinée des coûts de transport et de stockage dans un réseau logistique dyadique, multi-produits avec demande probabiliste". Phd thesis, INSA de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00695275.
Pełny tekst źródłaRoyer, Clément. "Algorithmes d'optimisation sans dérivées à caractère probabiliste ou déterministe : analyse de complexité et importance en pratique". Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30207/document.
Pełny tekst źródłaRandomization has had a major impact on the latest developments in the field of numerical optimization, partly due to the outbreak of machine learning applications. In this increasingly popular context, classical nonlinear programming algorithms have indeed been outperformed by variants relying on randomness. The cost of these variants is usually lower than for the traditional schemes, however theoretical guarantees may not be straightforward to carry out from the deterministic to the randomized setting. Complexity analysis is a useful tool in the latter case, as it helps in providing estimates on the convergence speed of a given scheme, which implies some form of convergence. Such a technique has also gained attention from the deterministic optimization community thanks to recent findings in the nonconvex case, as it brings supplementary indicators on the behavior of an algorithm. In this thesis, we investigate the practical enhancement of deterministic optimization algorithms through the introduction of random elements within those frameworks, as well as the numerical impact of their complexity results. We focus on direct-search methods, one of the main classes of derivative-free algorithms, yet our analysis applies to a wide range of derivative-free methods. We propose probabilistic variants on classical properties required to ensure convergence of the studied methods, then enlighten their practical efficiency induced by their lower consumption of function evaluations. Firstorder concerns form the basis of our analysis, which we apply to address unconstrained and linearly-constrained problems. The observed gains incite us to additionally take second-order considerations into account. Using complexity properties of derivative-free schemes, we develop several frameworks in which information of order two is exploited. Both a deterministic and a probabilistic analysis can be performed on these schemes. The latter is an opportunity to introduce supplementary probabilistic properties, together with their impact on numerical efficiency and robustness
Bonnard, Cécile. "Optimisation de potentiels statistiques pour un modèle d'évolution soumis à des contraintes structurales". Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2010. http://tel.archives-ouvertes.fr/tel-00495973.
Pełny tekst źródłaKsiążki na temat "Optimisation probabiliste"
Peter, Whittle, i Kelly F. P, red. Probability, statistics, and optimisation: A tribute to Peter Whittle. Chichester: Wiley, 1994.
Znajdź pełny tekst źródłaRoss, Sheldon M. Applied probability models with optimization applications. New York: London, 1992.
Znajdź pełny tekst źródłaRoss, Sheldon M. Applied probability models with optimization applications. New York: Dover Publications, 1992.
Znajdź pełny tekst źródłaSironi, Paolo. Modern portfolio management: From Markowitz to probabilistic scenario optimisation : goal-based and long-term portfolio choice. London: Risk Books, 2015.
Znajdź pełny tekst źródłaArora, Rajesh Kumar. Optimization: Algorithms and applications. Boca Raton: Taylor & Francis Group, 2015.
Znajdź pełny tekst źródłaL, Aarts E. H., i Lenstra J. K, red. Local search in combinatorial optimization. Chichester [England]: Wiley, 1997.
Znajdź pełny tekst źródłaOnn, Shmuel. Nonlinear discrete optimization: An algorithmic theory. Zürich, Switzerland: European Mathematical Society Publishing House, 2010.
Znajdź pełny tekst źródłaHansen, Eldon R. Global optimization using interval analysis. New York: M. Dekker, 1992.
Znajdź pełny tekst źródłaDavis, M. H. A. Markov models and optimization. London: Chapman & Hall, 1993.
Znajdź pełny tekst źródłaWhittle, Peter. Networks: Optimisation and Evolution (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, 2007.
Znajdź pełny tekst źródłaCzęści książek na temat "Optimisation probabiliste"
Illingworth, John, i Josef Kittler. "Optimisation Algorithms in Probabilistic Relaxation Labelling". W Pattern Recognition Theory and Applications, 109–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3_10.
Pełny tekst źródłaKhajwal, Basim, C. H. Luke Ong i Dominik Wagner. "Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing". W Programming Languages and Systems, 479–506. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_18.
Pełny tekst źródłade Vries, G. B., P. H. A. J. M. van Gelder i J. K. Vrijling. "Probabilistic Cost Optimisation of Soil Improvement Strategies". W Probabilistic Safety Assessment and Management, 3317–23. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_530.
Pełny tekst źródłaStamp, Mark. "Model-based optimisation of probability sampling designs". W Introduction to Machine Learning with Applications in Information Security, 231–62. Wyd. 2. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003264873-13.
Pełny tekst źródłaBrus, Dick J. "Model-based optimisation of probability sampling designs". W Spatial Sampling with R, 231–62. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003258940-13.
Pełny tekst źródłaSavić, Robert, i Uwe K. Rakowsky. "A Neuro-Fuzzy Reliability Optimisation Method Considering Life Cycle Costs". W Probabilistic Safety Assessment and Management, 1388–94. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_224.
Pełny tekst źródłaCamarinopoulos, L., G. Zioutas i E. Bora-Senta. "An Optimisation Technique For Robust Autoregressive Estimates". W Athens Conference on Applied Probability and Time Series Analysis, 102–14. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2412-9_8.
Pełny tekst źródłaBarros, Anne, Christophe Bérenguer i Antoine Grall. "Effect of false alarms on the optimisation of the maintenance decisions". W Probabilistic Safety Assessment and Management, 2833–39. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_454.
Pełny tekst źródłaBacharoudis, Konstantinos, Atanas Popov i Svetan Ratchev. "Application of Advanced Simulation Methods for the Tolerance Analysis of Mechanical Assemblies". W IFIP Advances in Information and Communication Technology, 153–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72632-4_11.
Pełny tekst źródłaZanin, Massimiliano, Marco Correia, Pedro A. C. Sousa i Jorge Cruz. "Probabilistic Constraint Programming for Parameters Optimisation of Generative Models". W Progress in Artificial Intelligence, 376–87. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23485-4_38.
Pełny tekst źródłaStreszczenia konferencji na temat "Optimisation probabiliste"
Stefanini, L., i F. J. Blom. "Safety Margin Optimisation by Probabilistic Analysis". W ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/pvp2017-65141.
Pełny tekst źródłaPuisa, R., i D. Vassalos. "Surrogate Optimisation of Probabilistic Subdivison Index". W Design and Operation of Passenger Ships 2011. RINA, 2011. http://dx.doi.org/10.3940/rina.pass.2011.12.
Pełny tekst źródłaFranke, Björn, Michael O'Boyle, John Thomson i Grigori Fursin. "Probabilistic source-level optimisation of embedded programs". W the 2005 ACM SIGPLAN/SIGBED conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1065910.1065922.
Pełny tekst źródłaNakiganda, Agnes M., Shahab Dehghan i Petros Aristidou. "A Data-Driven Optimisation Model for Designing Islanded Microgrids". W 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2022. http://dx.doi.org/10.1109/pmaps53380.2022.9810598.
Pełny tekst źródłaHolický, M. "Probabilistic optimisation of concrete cover exposed to carbonation". W ConcreteLife'06 - International RILEM-JCI Seminar on Concrete Durability and Service Life Planning: Curing, Crack Control, Performance in Harsh Environments. RILEM Publications SARL, 2006. http://dx.doi.org/10.1617/291214390x.040.
Pełny tekst źródłaShyam, RB Ashith, Peter Lightbody, Gautham Das, Pengcheng Liu, Sebastian Gomez-Gonzalez i Gerhard Neumann. "Improving Local Trajectory Optimisation using Probabilistic Movement Primitives". W 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8967980.
Pełny tekst źródłaEl Yafrani, Mohamed, Marcella Scoczynski, Myriam Delgado, Ricardo Luders, Inkyung Sung, Markus Wagner i Diego Oliva. "On Updating Probabilistic Graphical Models in Bayesian Optimisation Algorithm". W 2019 8th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2019. http://dx.doi.org/10.1109/bracis.2019.00062.
Pełny tekst źródłaPepper, Nick, Francesco Montomoli, Francesco Giacomel, Giovanna Cavazzini, Michele Pinelli, Nicola Casari i Sanjiv Sharma. "Uncertainty Quantification and Missing Data for Turbomachinery With Probabilistic Equivalence and Arbitrary Polynomial Chaos, Applied to Scroll Compressors". W ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16139.
Pełny tekst źródłaKim, Hyunsun A., i Robert A. Guyer. "Robust Topology Optimisation with Generalised Probability Distribution of Loading". W 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-1870.
Pełny tekst źródłaQureshi, Marij, Kwangkyu Yoo i M. H. Ferri Aliabadi. "Assessment of algorithms for the probabilistic optimisation of composite panels". W FRACTURE AND DAMAGE MECHANICS: Theory, Simulation and Experiment. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0034767.
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