Relevant bibliographies by topics / Iterative model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Iterative model'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Iterative model"

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Arkhipova, I. V. "Iterative-Taxis Intercategorial Interaction in German and Russian." Bulletin of Kemerovo State University 24, no. 2 (2022): 194–202. http://dx.doi.org/10.21603/2078-8975-2022-24-2-194-202.

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The article features iterative-taxis categorical situations of simultaneity and non-simultaneity in German and Russian. The functional and semantic categories of taxis and iteration are closely interrelated. The intersection and syncretic unification of these functional-semantic categories makes it possible to define the model of their intercategorial interaction as a model of intercategorical crossing. Intercategorical crossing of taxis and iterative categories determines the actualization of various iterative-taxis categorical situations of simultaneity and non-simultaneity. They are actuali
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Jain, Payal. "College Enquiry ChatBot Using Iterative Model." International Journal of Scientific Engineering and Research 7, no. 1 (2019): 80–83. https://doi.org/10.70729/ijser18568.

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Li, Xiaoli, Kang Wang, and Yang Li. "Multiple Model ILC for Continuous-Time Nonlinear Systems." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/984742.

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Multiple model iterative learning control (MMILC) method is proposed to deal with the continuous-time nonlinear system with uncertain and iteration-varying parameters. In this kind of control strategy, multiple models are established to cover the uncertainty of system; a switching mechanism is used to decide the most appropriate model for system in current iteration. For system operating iteratively in a fixed time interval with uncertain or jumping parameters, this kind of MMILC can improve the transient response and control property greatly. Asymptotical convergence is demonstrated theoretic
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Tian, Qihua, Yurong Zhang, Qunmei Dong, Xiangman Zhou, and Yixian Du. "Research on multi-stage iterative model solving method with resource optimization configuration." MATEC Web of Conferences 309 (2020): 05014. http://dx.doi.org/10.1051/matecconf/202030905014.

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Aiming at the problem of unreasonable resource allocation in the existing coupling design iterative model based on efficiency constraint, the resource equilibrium strategy was introduced into the iterative model. In order to reduce the time cost of product development, a multi-stage iterative model with optimal resource allocation was constructed and the optimal resource allocation of each task group was obtained by solving this model. Taking the design and development process of an air purifier as an example, the validity of this model was verified. The research shows that after introducing r
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Yao, Hua Ting, Xi Wang, and Xiang Xing Kong. "A Real-Time Transient Model of CF6 Turbofan Engine." Applied Mechanics and Materials 241-244 (December 2012): 1573–85. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1573.

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In traditional aeroengine modeling, the nonlinear equations of engine model are generally solved through iterative algorithm. However, due to the strong nonlinear characteristics of the equations, the iterative model often fails to converge at some points of the full envelope and has a poor real time performance. In order to solve the problems, this paper proposes a non-iterative modeling method based on volume effect. In this method, several variables and differential equations of volume dynamics in aeroengine are introduced to the nonlinear equations, as a result, the whole set of equations
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&NA;. "ITERATIVE COMPUTER MODEL." ASAIO Journal 44, no. 2 (1998): 25A. http://dx.doi.org/10.1097/00002480-199803000-00088.

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Li, Xia, Zhanqiang Song, Guiping Shen, Ying Huang, and Junyu Chen. "Diagnostic Value of Chest CT Images Based on Full Model Iterative Reconstruction Algorithm for Lung Cancer Patients." Scientific Programming 2021 (September 10, 2021): 1–7. http://dx.doi.org/10.1155/2021/5257682.

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Objective. To evaluate the value of low-dose CT scanning and full model iteration recombinant technology peripheral lung cancer in the paper using whole model iterative reconstruction algorithm and compare iterative model-wide restructuring, reorganization part of an iterative algorithm, affecting filtered back projection image quality. Method. Fifty-two patients with peripheral lung cancer, all of whom were diagnosed by pathological biopsy, were selected for the study. All patients received three scans of low-dose chest CT, next-low-dose, and low-dose, after which the raw data of three differ
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Kwon, Jung-Hyok, and Eui-Jik Kim. "Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things." Symmetry 12, no. 3 (2020): 454. http://dx.doi.org/10.3390/sym12030454.

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This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relev
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Malissiovas, Georgios, Frank Neitzel, Sven Weisbrich, and Svetozar Petrovic. "Weighted Total Least Squares (WTLS) Solutions for Straight Line Fitting to 3D Point Data." Mathematics 8, no. 9 (2020): 1450. http://dx.doi.org/10.3390/math8091450.

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In this contribution the fitting of a straight line to 3D point data is considered, with Cartesian coordinates xi, yi, zi as observations subject to random errors. A direct solution for the case of equally weighted and uncorrelated coordinate components was already presented almost forty years ago. For more general weighting cases, iterative algorithms, e.g., by means of an iteratively linearized Gauss–Helmert (GH) model, have been proposed in the literature. In this investigation, a new direct solution for the case of pointwise weights is derived. In the terminology of total least squares (TL
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Meger, Erin, and Abigail Raz. "The iterative independent model." Discrete Applied Mathematics 341 (December 2023): 242–56. http://dx.doi.org/10.1016/j.dam.2023.08.015.

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Dissertations / Theses on the topic "Iterative model"

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Segal, Aleksandr V. "Iterative Local Model Selection for tracking and mapping." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:8690e0e0-33c5-403e-afdf-e5538e5d304f.

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The past decade has seen great progress in research on large scale mapping and perception in static environments. Real world perception requires handling uncertain situations with multiple possible interpretations: e.g. changing appearances, dynamic objects, and varying motion models. These aspects of perception have been largely avoided through the use of heuristics and preprocessing. This thesis is motivated by the challenge of including discrete reasoning directly into the estimation process. We approach the problem by using Conditional Linear Gaussian Networks (CLGNs) as a generalization o
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Kou, Xiaochen. "An iterative approach for model selection in a class of semiparametric models." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/21551/.

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Clayton, Thomas F. "Enabling rapid iterative model design within the laboratory environment." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4023.

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This thesis presents a proof of concept study for the better integration of the electrophysiological and modelling aspects of neuroscience. Members of these two sub-disciplines collaborate regularly, but due to differing resource requirements, and largely incompatible spheres of knowledge, cooperation is often impeded by miscommunication and delays. To reduce the model design time, and provide a platform for more efficient experimental analysis, a rapid iterative model design method is proposed. The main achievement of this work is the development of a rapid model evaluation method based on pa
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Anderson, Taurug. "Iterative Learning Control model for a Resistive Wall Mode Active Controller." Thesis, KTH, Fusionsplasmafysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-125897.

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The EXTRAP T2R is a Reversed Field Pinch (RFP) device purposed to conduct magnetic confinement fusion research. Magnetic confinement works on the principle of isolating the hot plasma from the cool walls by locking in the plasma onto the magnetic field lines. Due to the generally unstable nature of plasma, the plasma column will shift off its centered position and encroach upon the cooling walls. To counteract this, a series of magnetic sensor coil are placed around the torus that serve as the input for another set of coils that will generate a magnetic field to force the plasma column back in
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Harte, Thomas James. "Discrete-time model-based Iterative Learning Control : stability, monotonicity and robustness." Thesis, University of Sheffield, 2007. http://etheses.whiterose.ac.uk/3624/.

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In this thesis a new robustness analysis for model-based Iterative Learning Control (ILC) is presented. ILC is a method of control for systems that are required to track a reference signal in a repetitive manner. The repetitive nature of such a system allows for the use of past information such that the control system iteratively learns control signals that give high levels of tracking. ILC algorithms that learn in a monotonic fashion are desirable as it implies that tracking performance is improved at each iteration. A number of model-based ILC algorithms are known to result in a monotonicall
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Vardhanabhuti, Varut. "Use of model-based iterative reconstruction (MBIR) techniques in computed tomography (CT)." Thesis, Exeter and Plymouth Peninsula Medical School, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757607.

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Over the past decades, the use of computed tomography (CT) has risen exponentially due to expanding need in medical diagnosis and treatment planning. The use of CT is not entirely without risk and involves theoretical risk from radiation. This has led to several advancement in dose reduction techniques. Most promising is the use of so called “iterative reconstruction” which is a post image acquisition step combining a software/hardware configuration that allows the images acquired to be processed more efficiently, eliminating noise which increases with low dose techniques. This thesis examines
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Nadir, Zeeshan. "A Model Based Iterative Reconstruction Approach to Tunable Diode Laser Absorption Tomography." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10842834.

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<p> Tunable diode laser absorption tomography (TDLAT) has emerged as a popular nonintrusive technique for simultaneous sensing of gas concentration and temperature. The gas concentration and temperature is computed by making light absorbance measurements using tunable diode lasers. Major challenges of TDLAT imaging include a highly nonlinear measurement process and availability of only a few light absorbance measurements. Therefore, TDLAT imaging of concentration and temperature is an ill-posed, nonlinear inverse problem. Conventional approaches to TDLAT primarily consist of making restrictive
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Benner, Peter, Enrique Quintana-Ortí, and Gregorio Quintana-Ortí. "Solving Linear Matrix Equations via Rational Iterative Schemes." Universitätsbibliothek Chemnitz, 2006. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200601460.

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We investigate the numerical solution of stable Sylvester equations via iterative schemes proposed for computing the sign function of a matrix. In particular, we discuss how the rational iterations for the matrix sign function can efficiently be adapted to the special structure implied by the Sylvester equation. For Sylvester equations with factored constant term as those arising in model reduction or image restoration, we derive an algorithm that computes the solution in factored form directly. We also suggest convergence criteria for the resulting iterations and compare the accuracy and perf
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Unlu, Mehmet Zubeyir. "Iterative deformable finite element model for nonrigid 3D PET/MRI breast image registration." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2006. http://proquest.umi.com/login?COPT=REJTPTU0NWQmSU5UPTAmVkVSPTI=&clientId=3739.

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Brown, Kathleen Annette. "An Iterative Needs Assessment/Evaluation Model for a Japanese University English-language Program." Diss., Temple University Libraries, 2009. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/66807.

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CITE/Language Arts<br>Ed.D.<br>The focus of this study is the development and implementation of the Iterative Needs Assessment/Evaluation Model for use as part of an English curriculum reform project at a four-year university in Japan. Three questions were addressed in this study: (a) what model components were necessary for use in a Japanese university setting; (b) what survey instruments would work with such a model; and (c) what needs would the stakeholders in the project report? The site for the study was a mid-sized private, four-year university in Japan. Set as an instrumental case stud
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Books on the topic "Iterative model"

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Diane, Ullius, and American Society for Training and Development., eds. Designing powerful training: The sequential-iterative model. Jossey-Bass/Pfeiffer, 1998.

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Waddell, Dale R. Estimating the weight of Douglas-fir tree boles and logs with an iterative computer model. U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1987.

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Waddell, Dale R. Estimating the weight of Douglas-fir tree boles and logs with an iterative computer model. U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1987.

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Wachspress, Eugene. The ADI Model Problem. Springer New York, 2013.

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Duflo, Marie. Random Iterative Models. Springer Berlin Heidelberg, 1997.

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Duflo, Marie. Random Iterative Models. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-12880-0.

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Duflo, Marie. Random iterative models. Springer, 1997.

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Pol, Marjon van der. Using a conditioned iterative generalised least squares estimator (CIGLS) to model a multilevel cost function and a discount function applied to repeated observations. Health Economics Research Unit, University of Aberdeen, 1997.

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Dubanov, Aleksandr. Computer simulation in pursuit problems. Publishing Center RIOR, 2022. http://dx.doi.org/10.29039/02102-6.

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Currently, computer simulation in virtual reality systems has a special status. In order for a computer model to meet the requirements of the tasks it models, it is necessary that the mathematical apparatus correctly describe the simulated phenomena.&#x0D; In this monograph, the simulation of pursuit problems is carried out. An adaptive modeling of the behavior of both pursuers and targets is carried out. An iterative calculation of the trajectories of the participants in the pursuit problem is carried out.&#x0D; The main attention is paid to the methods of pursuit and parallel rendezvous. The
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Chan, Raymond H. Iterative methods for overflow queueing models. Courant Institute of Mathematical Sciences, New York University, 1985.

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Book chapters on the topic "Iterative model"

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Precup, Radu-Emil, Raul-Cristian Roman, and Ali Safaei. "Iterative Feedback Tuning." In Data-Driven Model-Free Controllers. CRC Press, 2021. http://dx.doi.org/10.1201/9781003143444-2.

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Owens, David H. "Control Using Inverse Model Algorithms." In Iterative Learning Control. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6772-3_6.

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Åström, Karl J. "Model Uncertainty and Feedback." In Iterative Identification and Control. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0205-2_4.

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Richardson, Gary L., and Brad M. Jackson. "The Iterative Development Model." In Optimizing Project Work, Management, and Delivery. Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003431091-11.

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Fukuda, Mitsuo, and Seiichi Shin. "Model Reference Learning Control with a Wavelet Network." In Iterative Learning Control. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5629-9_11.

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Albertos, Pedro. "Model-based Iterative Control Design." In Iterative Identification and Control. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0205-2_6.

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Qu, Zu-Qing. "Iterative Methods for Dynamic Condensation." In Model Order Reduction Techniques. Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3827-3_6.

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Byrne, Charles. "Iterative Reconstruction Algorithms Based on Cross-Entropy Minimization." In Image Models (and their Speech Model Cousins). Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4056-3_1.

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Cueli, J. R. "Iterative Nonlinear Model Predictive Control." In Taming Heterogeneity and Complexity of Embedded Control. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9780470612217.ch12.

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Polotsky, Hanah, Lisa Williams, and Cheryl Kirk. "Introducing the Iterative Leadership Model." In Physician Leader. Productivity Press, 2023. http://dx.doi.org/10.4324/9780429352355-1.

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Conference papers on the topic "Iterative model"

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Paviot, Thomas, Virginie Fortineau, and Samir Lamouri. "Iterative Diagnosis-Driven Augmented Generation (IDDAG) for Programmatic 3D CAD." In 2nd Workshop on Model-based System Engineering and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013443500003896.

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Sayir, Jossy. "Design of non-binary decoders using the role model framework." In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613868.

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"AN ACTIVE STEREOSCOPIC SYSTEM FOR ITERATIVE 3D SURFACE RECONSTRUCTION." In 3D Model Aquisition and Representation. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002065500780084.

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Edgar A., Catalan Salgado, and Cornelio Yanez Marquez. "Non Iterative Hopfield Model." In 2006 Electronics, Robotics and Automotive Mechanics Conference. IEEE, 2006. http://dx.doi.org/10.1109/cerma.2006.67.

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Gramelsberger, Gabriele, Hendrik Kausch, Judith Michael, et al. "Enabling Informed Sustainability Decisions: Sustainability Assessment in Iterative System Modeling." In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023. http://dx.doi.org/10.1109/models-c59198.2023.00151.

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Yang, Yujing, Boqi Chen, Kua Chen, Gunter Mussbacher, and Dániel Varró. "Multi-step Iterative Automated Domain Modeling with Large Language Models." In MODELS Companion '24: ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. ACM, 2024. http://dx.doi.org/10.1145/3652620.3687807.

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Veres, S. M. "Modelling for control by iterative unfalsification." In IEE Seminar on Model Validation for Plant Control and Condition Monitoring. IEE, 2000. http://dx.doi.org/10.1049/ic:20000238.

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Wen Chen and F. N. Chowdhury. "Model reference iterative learning control." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1656456.

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Albertos, P., A. Esparza, and J. Romero. "Model-based iterative control design." In Proceedings of 2000 American Control Conference (ACC 2000). IEEE, 2000. http://dx.doi.org/10.1109/acc.2000.878673.

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Gaspar, P. "Iterative model-based mixed H." In UKACC International Conference on Control (CONTROL '98). IEE, 1998. http://dx.doi.org/10.1049/cp:19980306.

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Reports on the topic "Iterative model"

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Tseng, Lewis, and Nitin Vaidya. Iterative Approximate Byzantine Consensus under a Generalized Fault Model. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada564090.

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Donato, J. M. A comparison of iterative methods for a model coupled system of elliptic equations. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10180753.

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Waddell, Dale R., Dale L. Weyermann, and Michael B. Lambert. Estimating the weight of Douglas-fir tree boles and logs with an iterative computer model. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1987. http://dx.doi.org/10.2737/pnw-rp-374.

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Anderson, T. W., and R. P. Mentz. Iterative Procedures for Exact Maximum Likelihood Estimation in the First-Order Gaussian Moving Average Model. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230812.

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Maydykovskiy, Igor. Consciousness as a new form of the matter’s state. Intellectual Archive, 2021. http://dx.doi.org/10.32370/iaj.2555.

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The article discusses the physical model of the implicative form of Consciousness in the form of a holographic wave matrix, for which the material basis is directly the phase environment that fills the entire Space. It is shown that a similar form of Consciousness that exists outside the human brain can be represented as a kind of software shell that controls all forms of matter by implementing a fractal cyclic iterative algorithm. The condition for the completion of each iterative cycle at each scale level is the observance of the laws of symmetry that ensure the survival of the object in the
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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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Matus, Sean, and Daniel Gambill. Automation of gridded HEC-HMS model development using Python : initial condition testing and calibration applications. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/46126.

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The US Army Corps of Engineers’s (USACE) Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) rainfall-runoff model is widely used within the research community to develop both event-based and continuous rainfall-runoff models. The soil moisture accounting (SMA) algorithm is commonly used for long-term simulations. Depending on the final model setup, 12 to 18 parameters are needed to characterize the modeled watershed’s canopy, surface, soil, and routing processes, all of which are potential calibration parameters. HEC-HMS includes optimization tools to facilitate model calibrati
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Kennedy, Meaghan, Michaela Bonnett, and Teri Garstka. A Model for Technology-Enabled Community Resilience. Orange Sparkle Ball, 2024. http://dx.doi.org/10.61152/plcr9111.

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Introduction Tech-Enabled Community Resilience is an innovative model designed to enhance resilience and optimize impact in complex systems such as communities and ecosystems. The model leverages social network analysis and technology to visualize network dynamics, measure interactions, and implement targeted interventions. Model Structure The approach consists of two key stages: a Startup Phase focused on assembling champions and co-creating a shared vision, and a Steady-state Phase involving iterative measurement and intervention. By utilizing technology platforms for data collection and vis
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Dixon, Peter, Michael Jerie, and Maureen Rimmer. Modern Trade Theory for CGE Modelling: the Armington, Krugman and Melitz Models. GTAP Technical Paper, 2015. http://dx.doi.org/10.21642/gtap.tp36.

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This paper is for CGE modelers and others interested in modern trade theory. The Armington specification of trade, assuming country-level product differentiation, has been central to CGE modelling for 40 years. Starting in the 1980s with Krugman and more recently Melitz, trade theorists have preferred specifications with firm-level product differentiation. We draw out the connections between the Armington, Krugman and Melitz models, deriving them as successively less restrictive special cases of an encompassing model. We then investigate optimality properties of the Melitz model, demonstrating
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Gullberg, Grant T., Ronald H. Huesman, Bryan W. Reutter, Jinyi Qi, and Dilip N. Ghosh Roy. Estimation of the parameter covariance matrix for aone-compartment cardiac perfusion model estimated from a dynamic sequencereconstructed using map iterative reconstruction algorithms. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/928329.

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