Academic literature on the topic 'Inverse linear model'
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Journal articles on the topic "Inverse linear model"
BERNARD, James, and Mark PICKELMANN. "An Inverse Linear Model of a Vehicle." Vehicle System Dynamics 15, no. 4 (January 1986): 179–86. http://dx.doi.org/10.1080/00423118608968850.
Full textZhanatauov, S. U. "INVERSE MODEL OF MULTIPLE LINEAR REGRESSION ANALYSIS." Theoretical & Applied Science 60, no. 04 (April 30, 2018): 201–12. http://dx.doi.org/10.15863/tas.2018.04.60.38.
Full textBorneman, Joshua, Kuo-Ping Chen, Alex Kildishev, and Vladimir Shalaev. "Simplified model for periodic nanoantennae: linear model and inverse design." Optics Express 17, no. 14 (June 25, 2009): 11607. http://dx.doi.org/10.1364/oe.17.011607.
Full textAyala, A., M. Loewe, and R. Zamora. "Inverse magnetic catalysis in the linear sigma model." Journal of Physics: Conference Series 720 (May 2016): 012026. http://dx.doi.org/10.1088/1742-6596/720/1/012026.
Full textFang, Ximing. "A hybrid regularization model for linear inverse problems." Filomat 36, no. 8 (2022): 2739–48. http://dx.doi.org/10.2298/fil2208739f.
Full textHansen, Thomas Mejer, Andre G. Journel, Albert Tarantola, and Klaus Mosegaard. "Linear inverse Gaussian theory and geostatistics." GEOPHYSICS 71, no. 6 (November 2006): R101—R111. http://dx.doi.org/10.1190/1.2345195.
Full textCho, Jeong-Mok, Bong-Soo Yoo, and Joong-Seon Joh. "A Fuzzy Skyhook Algorithm Using Piecewise Linear Inverse Model." International Journal of Fuzzy Logic and Intelligent Systems 6, no. 3 (September 1, 2006): 190–96. http://dx.doi.org/10.5391/ijfis.2006.6.3.190.
Full textZhou, Huilin, Tao Ouyang, Yadan Li, Jian Liu, and Qiegen Liu. "Linear-Model-Inspired Neural Network for Electromagnetic Inverse Scattering." IEEE Antennas and Wireless Propagation Letters 19, no. 9 (September 2020): 1536–40. http://dx.doi.org/10.1109/lawp.2020.3008720.
Full textPenland, Cécile, and Ludmila Matrosova. "Expected and Actual Errors of Linear Inverse Model Forecasts." Monthly Weather Review 129, no. 7 (July 2001): 1740–45. http://dx.doi.org/10.1175/1520-0493(2001)129<1740:eaaeol>2.0.co;2.
Full textJiang, Wen, Yi Xin Su, and Dan Hong Zhang. "Research on Inverse Control of Active Magnetic Bearing Based on Fuzzy Inverse Model." Applied Mechanics and Materials 575 (June 2014): 744–48. http://dx.doi.org/10.4028/www.scientific.net/amm.575.744.
Full textDissertations / Theses on the topic "Inverse linear model"
Goudenege, Guillaume. "Développement de modèles d'optimisation de flux en logistique inverse : Applications aux contenants réutilisables." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2013. http://www.theses.fr/2013ECAP0014.
Full textIn an industrial world touched by a complicated economic environment, companies need to explore all opportunities for cost reduction and supply chain optimization. A recent optimization field developed in the literature concerns the concept of reverse logistics. This concept deals with the flows management through a supply chain in the opposite direction to the traditional one. It includes activities related to recycling, repair or products reuse. In partnership with the industrial of the “Chaire Supply Chain”, we are interested in optimizing these reverse flows by focusing more particularly on reusable containers. For that, we propose a literature review on the general concept of reverse logistics and develop a set of models covering combinations between single and multi-levels, single and multi-periods and single and multi-containers problems in order to optimize this type of returns within already defined supply chains. These models are then applied, either in a fictive way for a single-period one solved by a decomposition heuristic proposed for large logistics networks, or in a real way for multi-period models solved exactly and applied to our partners problematic. The purpose of these applications is to use these theoretical models in a real business in order to identify economic benefits but also environmental ones by taking into account emissions from these containers transportation and manufacturing
Rivers, Derick Lorenzo. "Dynamic Bayesian Approaches to the Statistical Calibration Problem." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3599.
Full textMarchand, Basile. "Assimilation de données et recalage rapide de modèles mécaniques complexes." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLN053/document.
Full textFor several years, the considerable changes that have occurredin computing tools have led to new practices in the simulation of mechanical structures. Among them, the motivation for this work is the Dynamic Data Driven Application Systems paradigm (DDDAS). The founding idea of this approach is to establish a dialogue between a physical system and its numericalmodel. The objective is then to (i) allow a calibration of the numerical model by means of measurements performed on the physical system; (ii) control the evolution of the physical system using theprediction given by numerical simulation. The major difficulty is to realize this dialogue in real time. This work focuses on the model updating step of the DDDAS paradigm. The problem is then to develop methods and tools to solve inverse problems taking into account various constraints, namely: (i) robustness with respect to corrupted data; (ii) genericity for considering a wide variety of problems and mechanical models; (iii) a reduced computation time in order to tend towards a real-time model updating.The starting point of this work is the modified Constitutive Relation Error, an energetic approach dedicated to the solution of inverse problems in mechanics, notably illustrated by its robustness with respect to measurement noises. First, in order to guarantee a fast identification process, we have coupled the modified Constitutive Relation Error with the PGD model reduction in the linear model framework, thus enabling a fast and automatic identification process. Then, in order to be applied to the DDDAS paradigm, we have developed an identification method based on a data assimilation process (the Kalman filter) and using the modified Constitutive Relation Error as an observer alwayswithin the framework of linear problems. We have then extended this data assimilation approach to the problem of the identification of parameter fields by introducing a separation of the spatial discretizations and by introducing tools resulting from the mesh adaptation framework. We have then addressed the problem of non-linear mechanical models, through damage and visco-plasticitymodels. To this end, we have first recast and extended the concept of the modified Constitutive Relation Error to this nonlinear material framework and we have implemented a dedicated resolution process, based on the LaTIn method. Finally, we have introduced this reformulation of the modified Constitutive Relation Error in the previously data assimilation method in order to process the model updating of nonlinear models
Rosecký, Martin. "Aplikace pokročilých regresních modelů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-382274.
Full textGranier, Bernard. "Restauration d'images perturbees par la turbulence atmospherique." Paris 11, 1996. http://www.theses.fr/1996PA112496.
Full textFontinele, Humberto Ãcaro Pinto. "Local models for inverse kinematics approximation of redundant robots: a performance comparison." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=16727.
Full textIn this dissertation it is reported the results of a comprehensive comparative study involving six local models applied to the task of learning the inverse kinematics of three redundant robotic arm (planar, PUMA 560 and Motoman HP6). The evaluated algorithms are the following ones: radial basis functions network (RBFN), local model network (LMN), SOMbased local linear mapping (LLM), local linear mapping over k-winners (K-SOM), local weighted regression (LWR) and counter propagation (CP). Each algorithm is evaluated with respect to its accuracy in estimating the joint angles given the cartesian coordinates which comprise end-effector trajectories within the robot workspace. A comprehensive evaluation of the performances of the aforementioned algorithms is carried out based on correlation analysis of the residuals. Finally, hypothesis testing procedures are also executed in order to verifying if there are significant differences in performance among the best algorithms.
Nesta dissertaÃÃo sÃo reportados os resultados de um amplo estudo comparativo envolvendo seis modelos locais aplicados à tarefa de aproximaÃÃo do modelo cinemÃtico inverso de 3 robÃs manipuladores (planar, PUMA 560 e Motoman HP6). Os modelos avaliados sÃo os seguintes: rede de funÃÃes de base radial (RBFN), rede de modelos locais (LMN), mapeamento linear local baseado em SOM (LLM), mapeamento linear local usando K vencedores (KSOM), regressÃo local ponderada (LWR) e rede counterpropagation (CP). Estes algoritmos sÃo avaliados quanto à acurÃcia na estimaÃÃo dos Ãngulos das juntas dos robÃs manipuladores em experimentos envolvendo a geraÃÃo de vÃrios tipos de trajetÃrias no espaÃo de trabalho dos referidos robÃs. Uma avaliaÃÃo abrangente do desempenho de cada algoritmo à feita com base na anÃlise dos resÃduos e testes de hipÃteses sÃo realizados para verificar a semelhanÃa estatistica entre os desempenhos dos melhores algoritmos.
Heijden, Luuk van der. "Determination of the food sources and of the role of meiofauna in soft-bottom intertidal habitats of the Marennes-Oléron Bay, France, and the Sylt-Rømø Bight, Germany : importance of the microphytobenthos-meiofauna pathway, highlighted by community structure, trophic markers and linear inverse food web models." Thesis, La Rochelle, 2018. http://www.theses.fr/2018LAROS030/document.
Full textMeiofauna play an important role in ecosystem processes in soft-bottom benthic habitats, e.g. food web dynamics, related to their highproduction, their intermediate trophic position and the energy they transfer towards higher trophic levels. The trophic linkages and flows of organic matter related to the meiofauna remain poorly known or taken into account. To better assess the role of meiofauna, the community structure and trophic relationships between food sources and meiofauna were determined in five intertidal soft-bottom habitats (i.e., mudflat, seagrass bed, sandflat) of the Marennes-Oléron Bay, France, and the Sylt-Rømø Bight, Germany, taking temporal variations into account. Meiofauna communities were dominated by nematodes and benthic copepods. Biomass of microphytobenthos and of sediment organic matter were two of the major drivers of community structure. The combination of trophic markers (i.e., stable isotopes, fatty acids) demonstrated that microphytobenthos and bacteria were the major food sources of meiofauna in the five habitats. Information from community structure assessments and trophic marker analyses were implemented in food web models. In all habitats, these models demonstrated that the main flow of carbon to meiofauna originated from microphytobenthos, highlighting negligible changes in meiofauna feeding behavior besides the large differences in availability and productivity of food sources between these habitats. All trophic groups of nematodes, except for selective deposit feeding nematodes, were highly selective and mainly fed on microphytobenthos, resulting in a high production and a short turn-over time of meiofauna. In conclusion, this thesis demonstrated the important role of meiofauna in soft-bottom habitats as well as the importance of the trophic pathway from microphytobenthos to meiofauna in the functioning of these food webs
Greiner, Eric. "Mise en oeuvre de méthodes de contrôle optimal pour l'assimilation de données in situ et satellitaires dans les modèles océaniques." Paris 6, 1993. http://www.theses.fr/1993PA066108.
Full textSandberg, Henrik. "Linear Time-Varying Systems: Modeling and Reduction." Licentiate thesis, Lund University, Department of Automatic Control, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-74720.
Full textQC 20120208
Brady, Kaitlyn. "Learning Curves in Emergency Ultrasonography." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/1150.
Full textBooks on the topic "Inverse linear model"
C, Hsuan Francis, ed. 2-inverses and their statistical application. New York: Springer-Verlag, 1988.
Find full textVoronin, Evgeniy, Aleksandr Chibunichev, and Yuriy Blohinov. Reliability of solving inverse problems of analytical photogrammetry. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/2010462.
Full textMas, André, and Besnik Pumo. Linear Processes for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.3.
Full textConnected Mathematics 2 thinking with mathematical models: Linear and inverse variation. USA, Boston, Massachusetts: pearson prentice hall, 2007.
Find full textFriel, Susan N., Glenda Lappan, James T. Fey, William M. Fitzgerald, and Elizabeth Difanis Phillips. Thinking with Mathematical Models: Linear and Inverse Variation (Connected Mathematics 2). Pearson Prentice Hall, 2006.
Find full textFriel, Susan N., Glenda Lappan, James T. Fey, William M. Fitzgerald, and Elizabeth Difanis Phillips. Thinking with Mathematical Models: Linear and Inverse Variation (Connected Mathematics 2). Pearson Prentice Hall, 2006.
Find full textFey, Fitzgerald Friel &. Phillips Lappan. Thinking with Mathematical Models (Linear & Inverse Variation) Teacher's Guide, Connected Mathematics 2. Pearson Prentice Hall, 2006.
Find full textPUBLISHER, PRENTICE HALL. CONNECTED MATHEMATICS 3 STUDENT EDITION GRADE 8 : THINKING with MATHEMATICAL MODELS: LINEAR and INVERSE VARIATION COPYRIGHT 2018. Savvas Learning Company, 2017.
Find full textConnected Mathematics 3 Student Edition Grade 8 : Thinking with Mathematical Models: Linear and Inverse Variation Copyright 2014. Savvas Learning Company, 2013.
Find full textPUBLISHER, PRENTICE HALL. Connected Mathematics 3 Spanish Student Edition Grade 8 : Thinking with Mathematical Models: Linear and Inverse Variation Copyright 2014. Savvas Learning Company, 2014.
Find full textBook chapters on the topic "Inverse linear model"
Choze, Sergio B., Rogerio R. Santos, Ariosto B. Jorge, and Guilherme F. Gomes. "An overview of Linear and Non-linear Programming methods for Structural Optimization." In Model-based and Signal-Based Inverse Methods, 65–106. Brasilia: Biblioteca Central da Universidade de Brasilia, 2022. http://dx.doi.org/10.4322/978-65-86503-71-5.c03.
Full textEvensen, Geir, Femke C. Vossepoel, and Peter Jan van Leeuwen. "Linear EnKF Update." In Springer Textbooks in Earth Sciences, Geography and Environment, 139–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96709-3_13.
Full textVardi, Y. "Applications of the EM Algorithm to Linear Inverse Problems with Positivity Constraints." In Image Models (and their Speech Model Cousins), 183–98. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4056-3_11.
Full textTrajkovska, Vera, Paul Swoboda, Freddie Åström, and Stefania Petra. "Graphical Model Parameter Learning by Inverse Linear Programming." In Lecture Notes in Computer Science, 323–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58771-4_26.
Full textMartínez, I., I. Ortiz, and C. Rodríguez. "Optimum Experimental Designs for a Modified Inverse Linear Model." In mODa 6 — Advances in Model-Oriented Design and Analysis, 171–81. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57576-1_19.
Full textNguyen, Ngoc Anh, Sorin Olaru, Pedro Rodriguez-Ayerbe, Morten Hovd, and Ion Necoara. "Fully Inverse Parametric Linear/Quadratic Programming Problems via Convex Liftings." In Developments in Model-Based Optimization and Control, 27–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26687-9_2.
Full textEyheramendy, Susana, and David Madigan. "A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization." In Complex Datasets and Inverse Problems, 76–91. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007. http://dx.doi.org/10.1214/074921707000000067.
Full textPillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Regularization for Linear System Identification." In Regularized System Identification, 135–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_5.
Full textCampos, Damián, Andrés Ajras, Lucas Goytiño, and Marcelo Piovan. "Bayesian Inversion of a Non-linear Dynamic Model for Stockbridge Dampers." In Proceedings of the XV Ibero-American Congress of Mechanical Engineering, 3–9. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-38563-6_1.
Full textBapat, R. B. "Generalized Inverses." In Linear Algebra and Linear Models, 31–36. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2739-0_4.
Full textConference papers on the topic "Inverse linear model"
Drouard, Vincent, Sileye Ba, and Radu Horaud. "Switching Linear Inverse-Regression Model for Tracking Head Pose." In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017. http://dx.doi.org/10.1109/wacv.2017.142.
Full textJasinska, Elzbieta. "ESTIMATION LINEAR MODEL USING BLOCK GENERALIZED INVERSE OF A MATRIX." In 13th SGEM GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING. Stef92 Technology, 2013. http://dx.doi.org/10.5593/sgem2013/bb2.v2/s09.022.
Full textSun, Shilong, Bert Jan Kooij, and Alexander G. Yarovoy. "Solving the PEC inverse scattering problem with a linear model." In 2016 URSI International Symposium on Electromagnetic Theory (EMTS). IEEE, 2016. http://dx.doi.org/10.1109/ursi-emts.2016.7571336.
Full textAldrian, Oswald, and William A. P. Smith. "Inverse rendering in SUV space with a linear texture model." In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, 2011. http://dx.doi.org/10.1109/iccvw.2011.6130337.
Full textSerrani, A. "Output regulation for over-actuated linear systems via inverse model allocation." In 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6426209.
Full textRamirez-Martinez, O. L., E. A. Martinez-Garcia, R. E. Mohan, and J. K. Sheba. "Mobile robot adaptive trajectory control: Non-linear path model inverse transformation for model reference." In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014. http://dx.doi.org/10.1109/icarcv.2014.7064420.
Full textBraghin, Francesco, Simone Cinquemani, and Ferruccio Resta. "Power Harvesting Through Magnetostrictive Devices: A Linear Model." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-24888.
Full textLee, K. Y., and Hee-Sang Ko. "Power system stabilization using a free model based inverse dynamic linear controller." In Proceedings of Power Engineering Society Summer Meeting. IEEE, 2001. http://dx.doi.org/10.1109/pess.2001.970190.
Full textZayani, R., Rim Guedria, and R. Bouallegue. "Compensation of the OFDM non-linear distortions by the inverse model method." In 8th International Conference on Advanced Communication Technology. IEEE, 2006. http://dx.doi.org/10.1109/icact.2006.206412.
Full textChebotarev, Alexander, Pavel Mesenev, and Andrey Kovtanyuk. "Inverse problem with unknown sources for a quasi-linear complex heat transfer model." In 2023 Days on Diffraction (DD). IEEE, 2023. http://dx.doi.org/10.1109/dd58728.2023.10325734.
Full textReports on the topic "Inverse linear model"
Yu, Guoshen, Guillermo Sapiro, and Stephane Mallat. Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada540722.
Full textPoppeliers, Christian, Katherine Anderson Aur, and Leiph Preston. The use of atmospheric prediction models to invert infrasound for linear-equivalent time domain moment tensors: Source Physics Experiment Phase 1. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1468382.
Full textKrause, Thomas, Mehrdad Keshefi, Ross Underhill, and Lynann Clapham. PR652-203801-R02 Magnetic Object Model for Large Standoff Magnetometry Measurement. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2021. http://dx.doi.org/10.55274/r0012151.
Full textJury, William A., and David Russo. Characterization of Field-Scale Solute Transport in Spatially Variable Unsaturated Field Soils. United States Department of Agriculture, January 1994. http://dx.doi.org/10.32747/1994.7568772.bard.
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