Literatura científica selecionada sobre o tema "Fixed-Time and robust estimation"
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Artigos de revistas sobre o assunto "Fixed-Time and robust estimation"
OLIINYK, VIACHESLAV, e VOLODYMYR LUKIN. "USE OF SIMILARITY METRICS IN ROBUST TIME DELAY ESTIMATION". Herald of Khmelnytskyi National University. Technical sciences 319, n.º 2 (27 de abril de 2023): 224–30. http://dx.doi.org/10.31891/2307-5732-2023-319-1-224-230.
Texto completo da fonteThombs, Ryan P. "A Guide to Analyzing Large N, Large T Panel Data". Socius: Sociological Research for a Dynamic World 8 (janeiro de 2022): 237802312211176. http://dx.doi.org/10.1177/23780231221117645.
Texto completo da fonteWu, Tao, Zhengjiang Liu e Guoyou Shi. "Practical Fixed-Time Robust Containment Control of Multi-ASVs with Collision Avoidance". Journal of Marine Science and Engineering 12, n.º 12 (23 de dezembro de 2024): 2363. https://doi.org/10.3390/jmse12122363.
Texto completo da fonteYoussef, Ahmed Hassen, Mohamed Reda Abonazel e Elsayed G. Ahmed. "Robust M Estimation for Poisson Panel Data Model with Fixed Effects: Method, Algorithm, Simulation, and Application". Statistics, Optimization & Information Computing 12, n.º 5 (3 de junho de 2024): 1292–305. http://dx.doi.org/10.19139/soic-2310-5070-1996.
Texto completo da fonteMagnussen, Steen. "Robust fixed-count density estimation with virtual plots". Canadian Journal of Forest Research 44, n.º 4 (abril de 2014): 377–82. http://dx.doi.org/10.1139/cjfr-2013-0288.
Texto completo da fonteChai, Dashuai, Yipeng Ning, Shengli Wang, Wengang Sang, Jianping Xing e Jingxue Bi. "A Robust Algorithm for Multi-GNSS Precise Positioning and Performance Analysis in Urban Environments". Remote Sensing 14, n.º 20 (15 de outubro de 2022): 5155. http://dx.doi.org/10.3390/rs14205155.
Texto completo da fonteXi, Axing, e Yuanli Cai. "A Nonlinear Finite-Time Robust Differential Game Guidance Law". Sensors 22, n.º 17 (2 de setembro de 2022): 6650. http://dx.doi.org/10.3390/s22176650.
Texto completo da fonteNakamori, Seiichi. "Robust Recursive Least-Squares Fixed-Point Smoother and Filter using Covariance Information in Linear Continuous-Time Stochastic Systems with Uncertainties". WSEAS TRANSACTIONS ON SIGNAL PROCESSING 20 (13 de maio de 2024): 56–66. http://dx.doi.org/10.37394/232014.2024.20.2.
Texto completo da fonteMentz, Raúl P., e Carlos I. Martínez. "Robust estimation in time series". Test 11, n.º 2 (dezembro de 2002): 385–404. http://dx.doi.org/10.1007/bf02595713.
Texto completo da fonteGuerrier, Stephane, Roberto Molinari e Maria-Pia Victoria-Feser. "Estimation of Time Series Models via Robust Wavelet Variance". Austrian Journal of Statistics 43, n.º 4 (13 de junho de 2014): 267–77. http://dx.doi.org/10.17713/ajs.v43i4.45.
Texto completo da fonteTeses / dissertações sobre o assunto "Fixed-Time and robust estimation"
Zhang, Yuqing. "Fixed-time algebraic distributed state estimation for linear systems". Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2025. http://www.theses.fr/2025ISAB0001.
Texto completo da fonteIn recent decades, the widespread deployment of networked embedded sensors with communication capabilities in large-scale systems has drawn significant attentions fromresearchers to the field of distributed estimation. This thesis aims to develop a fixed-time algebraic distributed state estimation method for both integer-order linear time-varying systems and fractional-order linear-invariant systems in noisy environments, by designing a set of reduced-order local estimators at the networked sensors.To achieve this, we first introduce a distributed estimation scheme by defining a recovered node set at each sensor node, based on a digraph assumption that is more relaxed than the strongly connected one. Using this recovered set, we construct an invertible transformation for the observability decomposition to identify each node’s local observable subsystem. Additionally, this transformation allows for a distributed representation of the entire system state at each node by a linear combination of its own local observable state and those of the nodes in its recovered set. This ensures that each node can achieve the distributed state estimation, provided that the estimations for the set of local observable states are ensured. As a result, this distributed scheme focuses on estimating the local observable states, enabling distributed estimation across the sensor network.Building on this foundation, to address the fixed-time algebraic state estimation for each identified local observable subsystem, different modulating functions estimation methods are investigated to derive the initial-condition-independent algebraic formulas, making them effective as reduced-order local fixed-time estimators. For integer-order linear time-varying systems, the transformation used in developing distributed estimation scheme yields a linear time-varying partial observable normal form. The generalized modulating functions method is then applied to estimate each local observable state through algebraic integral formulas of system outputs and their derivatives. For fractional-order linear-invariant systems, another transformation is used to convert each identified local observable subsystem into a fractional-order observable normal form, allowing for the application of the fractional-order generalized modulating functions estimation method. This method directly computes algebraic integral formulas for local observable pseudo-state variables.Subsequently, by combining these algebraic formulas with the derived distributed representation, we achieve the fixed-time algebraic distributed state estimation for the studied systems. Additionally, an error analysis is conducted to demonstrate the robustness of the designed distributed estimator in the presence of both continuous process and measurement noises, as well as discrete measurement noises. Finally, several simulation examples are provided to validate the effectiveness of the proposed distributed estimation scheme
Copeland, Andrew David 1978. "Robust motion estimation in the presence of fixed pattern noise". Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87395.
Texto completo da fonteIncludes bibliographical references (p. 41-42).
by Andrew David Copeland.
M.Eng.
Kwan, Tan Hwee. "Robust estimation for structural time series models". Thesis, London School of Economics and Political Science (University of London), 1990. http://etheses.lse.ac.uk/2809/.
Texto completo da fonteSinha, Sanjoy Kumar. "Some aspects of robust estimation in time series analysis". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ57354.pdf.
Texto completo da fonteZheng, Xueying, e 郑雪莹. "Robust joint mean-covariance model selection and time-varying correlation structure estimation for dependent data". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899703.
Texto completo da fontepublished_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
Kovac, Arne. "Wavelet thresholding for unequally time-spaced data". Thesis, University of Bristol, 1999. http://hdl.handle.net/1983/2088715a-7792-4032-bb76-83e3b0389b94.
Texto completo da fonteSkoglund, Johan. "Robust Real-Time Estimation of Region Displacements in Video Sequences". Licentiate thesis, Linköping : Department of Electrical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8006.
Texto completo da fonteLaMaire, Richard O. "Robust time and frequency domain estimation methods in adaptive control". Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14795.
Texto completo da fonteMICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.
Supported, in part, by the NASA Ames & Langley Research Centers, the Office of Naval Research, and the National Science Foundation.
Bibliography: v. 2, leaves 334-337.
by Richard Orville LaMaire.
Ph.D.
Staerman, Guillaume. "Functional anomaly detection and robust estimation". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT021.
Texto completo da fonteEnthusiasm for Machine Learning is spreading to nearly all fields such as transportation, energy, medicine, banking or insurance as the ubiquity of sensors through IoT makes more and more data at disposal with an ever finer granularity. The abundance of new applications for monitoring of complex infrastructures (e.g. aircrafts, energy networks) together with the availability of massive data samples has put pressure on the scientific community to develop new reliable Machine-Learning methods and algorithms. The work presented in this thesis focuses around two axes: unsupervised functional anomaly detection and robust learning, both from practical and theoretical perspectives.The first part of this dissertation is dedicated to the development of efficient functional anomaly detection approaches. More precisely, we introduce Functional Isolation Forest (FIF), an algorithm based on randomly splitting the functional space in a flexible manner in order to progressively isolate specific function types. Also, we propose the novel notion of functional depth based on the area of the convex hull of sampled curves, capturing gradual departures from centrality, even beyond the envelope of the data, in a natural fashion. Estimation and computational issues are addressed and various numerical experiments provide empirical evidence of the relevance of the approaches proposed. In order to provide recommendation guidance for practitioners, the performance of recent functional anomaly detection techniques is evaluated using two real-world data sets related to the monitoring of helicopters in flight and to the spectrometry of construction materials.The second part describes the design and analysis of several robust statistical approaches relying on robust mean estimation and statistical data depth. The Wasserstein distance is a popular metric between probability distributions based on optimal transport. Although the latter has shown promising results in many Machine Learning applications, it suffers from a high sensitivity to outliers. To that end, we investigate how to leverage Medians-of-Means (MoM) estimators to robustify the estimation of Wasserstein distance with provable guarantees. Thereafter, a new statistical depth function, the Affine-Invariant Integrated Rank-Weighted (AI-IRW) depth is introduced. Beyond the theoretical analysis carried out, numerical results are presented, providing strong empirical confirmation of the relevance of the depth function proposed. The upper-level sets of statistical depths—the depth-trimmed regions—give rise to a definition of multivariate quantiles. We propose a new discrepancy measure between probability distributions that relies on the average of the Hausdorff distance between the depth-based quantile regions w.r.t. each distribution and demonstrate that it benefits from attractive properties of data depths such as robustness or interpretability. All algorithms developed in this thesis are open-sourced and available online
Chapman, Michael Addison. "Adaptation and Installation of a Robust State Estimation Package in the Eef Utility". Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/31432.
Texto completo da fonteMaster of Science
Livros sobre o assunto "Fixed-Time and robust estimation"
Ladlow, Peter Thomas. Robust parameter estimation techniques for time-varying processes. Birmingham: University of Birmingham, 1998.
Encontre o texto completo da fonteSubrahmanyam, Allamaraju, e Ganti Prasada Rao. Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation. Taylor & Francis Group, 2019.
Encontre o texto completo da fonteRobust time and frequency domain estimation methods in adaptive control. Cambridge, Mass: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, 1987.
Encontre o texto completo da fonteSubrahmanyam, Allamaraju, e Ganti Prasada Rao. Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation. Taylor & Francis Group, 2019.
Encontre o texto completo da fonteSubrahmanyam, Allamaraju, e Ganti Prasada Rao. Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation. Taylor & Francis Group, 2019.
Encontre o texto completo da fonteSubrahmanyam, Allamaraju, e Ganti Prasada Rao. Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation. Taylor & Francis Group, 2019.
Encontre o texto completo da fonteAnjum, Rani Lill, e Stephen Mumford. Same Cause, Same Effect. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0005.
Texto completo da fonteHankin, David, Michael S. Mohr e Kenneth B. Newman. Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Fixed-Time and robust estimation"
Subrahmanyam, Allamaraju, e Ganti Prasada Rao. "Robust Parameter Estimation". In Identification of Continuous-Time Systems, 63–79. First edition. | New York, N.Y. : CRC Press/Taylor & Francis Group, 2020. | Series: Engineering systems and sustainability: CRC Press, 2019. http://dx.doi.org/10.1201/9780429352850-4.
Texto completo da fonteMangoubi, Rami S. "Discrete-Time Robust Estimation". In Robust Estimation and Failure Detection, 43–84. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1586-1_3.
Texto completo da fonteAgostinelli, C. "Robust Time Series Estimation via Weighted Likelihood". In Developments in Robust Statistics, 1–16. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-642-57338-5_1.
Texto completo da fonteSeong, Junyeong, Sungjun Park e Kunsoo Huh. "Robust Lane Keeping Control with Estimation of Cornering Stiffness and Model Uncertainty". In Lecture Notes in Mechanical Engineering, 272–78. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_39.
Texto completo da fontePetersen, Ian R., e Andrey V. Savkin. "Discrete-Time Set-Valued State Estimation". In Robust Kalman Filtering for Signals and Systems with Large Uncertainties, 71–87. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1594-3_5.
Texto completo da fonteWu, Renbiao, Qiongqiong Jia, Lei Yang e Qing Feng. "Application of RELAX in Time Delay Estimation". In Principles and Applications of RELAX: A Robust and Universal Estimator, 101–57. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6932-2_4.
Texto completo da fonteda Silva, Nuno Pinho, e João Paulo Costeira. "Robust Global Mosaic Topology Estimation for Real-Time Applications". In Lecture Notes in Computer Science, 1250–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559573_151.
Texto completo da fonteGershon, Eli, e Uri Shaked. "Robust Estimation of Linear-Switched Systems with Dwell Time". In Advances in H∞ Control Theory, 61–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16008-1_5.
Texto completo da fonteCheng, Yu, Ilias Diakonikolas e Rong Ge. "High-Dimensional Robust Mean Estimation in Nearly-Linear Time". In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2755–71. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2019. http://dx.doi.org/10.1137/1.9781611975482.171.
Texto completo da fonteReich, Sebastian. "Frequentist Perspective on Robust Parameter Estimation Using the Ensemble Kalman Filter". In Mathematics of Planet Earth, 237–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18988-3_15.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Fixed-Time and robust estimation"
Koizumi, Kakeru, e Hiroshi Watanabe. "Event-based Robust 3D Pose Estimation Using Time Series Data". In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), 1223–27. IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10731089.
Texto completo da fonteLv, Yuezhang, Yunzhou Zhang, Xiaoyu Zhao, Wu Li, Jian Ning e Yang Jin. "CTA-LO: Accurate and Robust LiDAR Odometry Using Continuous-Time Adaptive Estimation". In 2024 IEEE International Conference on Robotics and Automation (ICRA), 12034–40. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611453.
Texto completo da fontePnevmatikakis, Aristodemos, e Lazaros Polymenakos. "Robust Estimation of Background for Fixed Cameras". In 2006 15th International Conference on Computing. IEEE, 2006. http://dx.doi.org/10.1109/cic.2006.63.
Texto completo da fonteLee, You-Seok, e Hyoung-Nam Kim. "Noise-Robust Channel Estimation for DVB-T Fixed Receptions". In 2007 Digest of Technical Papers International Conference on Consumer Electronics. IEEE, 2007. http://dx.doi.org/10.1109/icce.2007.341562.
Texto completo da fonteRoy, Shibdas, Ian R. Petersen e Elanor H. Huntington. "Adaptive continuous homodyne phase estimation using robust fixed-interval smoothing". In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580312.
Texto completo da fonteVerling, Sebastian L., e Roland Siegwart. "Robust Wind Estimation for Fixed Wing UAVs in Surveying Applications". In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-0373.
Texto completo da fonteMcPhee, Hamish, Jean-Yves Tourneret, David Valat, Philippe Paimblanc, Jérome Delporte e Yoan Grégoire. "A Robust Time Scale Based on Maximum Likelihood Estimation". In 54th Annual Precise Time and Time Interval Systems and Applications Meeting. Institute of Navigation, 2023. http://dx.doi.org/10.33012/2023.18701.
Texto completo da fonteAbu Bakar, Nor Mazlina, e Habshah Midi. "The Applications of Robust Estimation in Fixed Effect Panel Data Model". In Proceedings of the 1st Aceh Global Conference (AGC 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/agc-18.2019.54.
Texto completo da fonteWenqiang Liu e Zili Deng. "Robust steady-state Kalman filter for uncertain discrete-time system". In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280188.
Texto completo da fonteZhang, Zhaoyu, e Haibin Duan. "Robust Adaptive Filter for Time-Varying Parameters Estimation in Integrated Navigation of Fixed-Wing Aerial Robot*". In 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2023. http://dx.doi.org/10.1109/robio58561.2023.10354695.
Texto completo da fonteRelatórios de organizações sobre o assunto "Fixed-Time and robust estimation"
Galindo, Arturo, e Marcela Meléndez Arjona. Corporate Tax Stimulus and Investment in Colombia. Inter-American Development Bank, abril de 2010. http://dx.doi.org/10.18235/0010933.
Texto completo da fonteRojas-Bernal, Alejandro, e Mauricio Villamizar-Villegas. Pricing the exotic: Path-dependent American options with stochastic barriers. Banco de la República de Colombia, março de 2021. http://dx.doi.org/10.32468/be.1156.
Texto completo da fonteSchling, Maja, Nicolás Pazos, Leonardo Corral e Marisol Inurritegui. The Effects of Tenure Security on Women's Empowerment and Food Security: Evidence From a Land Regularization Program in Ecuador. Inter-American Development Bank, dezembro de 2023. http://dx.doi.org/10.18235/0005355.
Texto completo da fonteBaltagi, Badi H., Georges Bresson, Anoop Chaturvedi e Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, janeiro de 2023. http://dx.doi.org/10.54932/ufyn4045.
Texto completo da fontePinkovskiy, Maxim, Xavier Sala-i-Martin, Kasey Chatterji-Len e William Nober. Inequality Within Countries is Falling: Underreporting Robust Estimates of World Poverty, Inequality, and the Global Distribution of Income. Federal Reserve Bank of New York, setembro de 2024. http://dx.doi.org/10.59576/sr.1125.
Texto completo da fonteStucchi, Rodolfo, Alessandro Maffioli, Sofía Rojo e Victoria Castillo. Knowledge Spillovers of Innovation Policy through Labor Mobility: An Impact Evaluation of the FONTAR Program in Argentina. Inter-American Development Bank, fevereiro de 2014. http://dx.doi.org/10.18235/0011534.
Texto completo da fonteArizala, Francisco, Eduardo A. Cavallo e Arturo Galindo. Financial Development and TFP Growth: Cross-Country and Industry-Level Evidence. Inter-American Development Bank, junho de 2009. http://dx.doi.org/10.18235/0010917.
Texto completo da fonteChong, Alberto E., e Eliana La Ferrara. Television and Divorce: Evidence from Brazilian Novelas. Inter-American Development Bank, janeiro de 2009. http://dx.doi.org/10.18235/0010906.
Texto completo da fonteGonzález, Francisco, José E. Gutiérrez e José María Serena. Shadow seniority? Lending relationships and borrowers’ selective default. Madrid: Banco de España, junho de 2024. http://dx.doi.org/10.53479/36695.
Texto completo da fonteTorero, Máximo, e Jaime Saavedra-Chanduví. Union Density Changes and Union Effects on Firm Performance in Peru. Inter-American Development Bank, setembro de 2002. http://dx.doi.org/10.18235/0011249.
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