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Готові списки джерел за темами / Iterative model / Статті в журналах

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Статті в журналах з теми "Iterative model"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

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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2

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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3

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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4

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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5

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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6

&NA;. "ITERATIVE COMPUTER MODEL." ASAIO Journal 44, no. 2 (1998): 25A. http://dx.doi.org/10.1097/00002480-199803000-00088.

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7

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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8

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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9

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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10

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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11

He, Yao, Deqiang Yao, Sheng Huang, et al. "Improved direct-method aided iterative dual-space model completion." Acta Crystallographica Section A Foundations and Advances 70, a1 (2014): C332. http://dx.doi.org/10.1107/s2053273314096673.

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It has been proved that direct methods are efficient in providing phase constraints within the dual-space phase-model iterative framework [1]. The program OASIS is used for the direct-method implementation. Two kinds of iterative direct methods are performed by OASIS. One involves the use of SAD/SIR information [2], while the other dose not [3]. Improvements have been made on both kinds of iterative direct methods. First, the Srinivasan's weighting function replaces previously used Sim's weighting function in the direct-method phase derivation leading to better estimation of phases. This affec

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12

Pery, Marcin, and Robert Waszkowski. "A Model and Quantitative Framework for Evaluating Iterative Steganography." Entropy 26, no. 12 (2024): 1130. https://doi.org/10.3390/e26121130.

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This study presents a detailed characterization of iterative steganography, a unique class of information-hiding techniques, and proposes a formal mathematical model for their description. A novel quantitative measure, the Incremental Information Function (IIF), is introduced to evaluate the process of information gain in iterative steganographic methods. The IIF offers a comprehensive framework for analyzing the step-by-step process of embedding information into a cover medium, focusing on the cumulative effects of each iteration in the encoding and decoding cycles. The practical application

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13

Dai, Ya-Di, and Hui-Guo Zhang. "Non-Iterative Estimation of Multiscale Geographically and Temporally Weighted Regression Model." Mathematics 13, no. 9 (2025): 1446. https://doi.org/10.3390/math13091446.

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The Multiscale Geographically and Temporally Weighted Regression model overcomes the limitation of estimating spatiotemporal variation characteristics of regression coefficients for different variables under a single scale, making it a powerful tool for exploring the spatiotemporal scale characteristics of regression relationships. Currently, the most widely used estimation method for multiscale spatiotemporal geographically weighted models is the backfitting-based iterative approach. However, the iterative process of this method leads to a substantial computational burden and the accumulation

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14

Wang, Derui, and Jingyu Hou. "Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions." Journal of Bioinformatics and Computational Biology 13, no. 05 (2015): 1550026. http://dx.doi.org/10.1142/s0219720015500262.

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Protein–protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this

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15

Sangeeta, Sitender, Rachna Jain, and Ankita Bansal. "Bug Report Analytics for Software Reliability Assessment using Hybrid Swarm – Evolutionary Algorithm." e-Informatica Software Engineering Journal 19, no. 1 (2025): 250101. http://dx.doi.org/10.37190/e-inf250101.

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Background: With the growing advances in the digital world, software development demands are increasing at an exponential rate. To ensure reliability of the software, high-performance tools for bug report analysis are needed. Aim: This paper proposes a new ‘Iterative Software Reliability’ model based on one of the most recent Software Development Life Cycle (SDLC) approach. Method: The proposed iterative failure rate model assumes that new functionality enhancement occurs in each iteration of software development and accordingly design modification is made at each stage of software development

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16

Azevedo, Leonardo. "Model reduction in geostatistical seismic inversion with functional data analysis." GEOPHYSICS 87, no. 1 (2021): M1—M11. http://dx.doi.org/10.1190/geo2021-0096.1.

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In subsurface modeling and characterization, predicting the spatial distribution of subsurface elastic properties is commonly achieved by seismic inversion. Stochastic seismic inversion methods, such as iterative geostatistical seismic inversion (GSI), are widely applied to this end. Global iterative GSI methods are computationally expensive because they require, at a given iteration, the stochastic sequential simulation of the entire inversion grid at once multiple times. Functional data analysis (FDA) is a well-established statistical method suited to model long-term and noisy temporal serie

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17

Wang, Xinrui, Chengcheng Zhu, Jing Li, Andrew J. Degnan, Tao Jiang, and Jianping Lu. "Knowledge-based iterative model reconstruction." Medicine 97, no. 30 (2018): e11514. http://dx.doi.org/10.1097/md.0000000000011514.

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18

Xiaoli, Li, and Zhang Wen. "Multiple model iterative learning control." Neurocomputing 73, no. 13-15 (2010): 2439–45. http://dx.doi.org/10.1016/j.neucom.2009.12.035.

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19

Yunak, O., M. Klymash, O. Shpur, and V. Mrak. "MATHEMATICAL MODEL OF FRACTAL STRUCTURES RECOGNITION USING NEURAL NETWORK TECHNOLOGY." Information and communication technologies, electronic engineering 3, no. 1 (2023): 1–9. http://dx.doi.org/10.23939/ictee2023.01.001.

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The article goes about the methods of training a neural network to recognize fractal structures with the rotation of iteration elements by means of an improved randomized system of iteration functions. Parameters of fractal structures are used to calculate complex parameters of physical phenomena. They are an effective tool in scientific works and used to calculate quantitative indicators in technical tasks. The calculation of these parameters is a very difficult mathematical problem. This is caused by the fact that it is very difficult to describe the mathematical model of the fractal image,

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20

Li, Yi Bing, Rui Yang, and Fang Ye. "Price Game Based Spectrum Sharing in Cognitive Radio Networks." Advanced Materials Research 225-226 (April 2011): 632–36. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.632.

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The competitive price model is applied in the analysis of spectrum sharing in cognitive radio networks. As the prices of shared spectrum are determined independently by primary users in practical application, to deal with the solution of equilibrium price without acknowledgement of current price strategies from other primary users, the iterative method is used to calculate price of the shared spectrum. In order to achieve better characteristics, an exponential iterative method for the competitive price based spectrum sharing model is proposed. It can achieve the desired spectrum price only wit

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21

Fu, Yu, Li Li, and Yujin Hu. "An Efficient and Robust Topology Optimization Method for Thermoelastically Damped Microresonators." Applied Sciences 13, no. 15 (2023): 8811. http://dx.doi.org/10.3390/app13158811.

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The challenges of computational cost and robustness are critical obstacles in topology optimization methods, particularly for the iterative process of optimizing large-scale multiphysical structures. This study proposes an efficient and robust topology optimization method for minimizing the thermoelastic damping of large-scale microresonators. An evolutionary structural optimization method is adopted to passively determine the search direction of optimizing large-scale thermoelastic structures. To efficiently reduce the computational cost of the iterative process of an optimizing process, a mo

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22

Li, Heng Jie, Xiao Hong Hao, Xian Jun Du, and Ya Rong Jin. "A Clonal Selection Algorithm Based Fuzzy Optimal Iterative Learning Control Algorithm." Advanced Materials Research 490-495 (March 2012): 329–33. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.329.

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In order to realize effective tracking of output of non-linear plants with model uncertainty in specified time domain, a clonal selection algorithm based fuzzy optimal iterative learning control algorithm is proposed. In the algorithm, a clonal selection algorithm is employed to search optimal input for next iteration, and another clonal selection algorithm is used to update the parameters of Takagi-Sugeno-Kang fuzzy system model of the plant. Simulations show that the proposed method converges faster than GA-ILC in iterative domain，and is able to deal with model uncertainty well

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23

Cui, Yuan, Shan Gao, and Junpeng Zhang. "ITERATIVE MUSIC FOR HIGHLY CORRELATED EEG/MEG SOURCE LOCALIZATION." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (2013): 1350019. http://dx.doi.org/10.4015/s1016237213500191.

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This study presented an iterative MUSIC (Multiple Signal Classification) for highly correlated EEG source localization. By suppressing the equivalent false source, the approximate true source location information was obtained. And then, by iteratively suppressing source found in the last iteration, eventually, both of the sources were identified. The method is designed to tackle highly correlated sources, for example, bilateral activations at primary auditory/auditory cortices, at which cases conventional MUSIC has difficulty. Compared with other similar methods, the presented one needs less c

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24

Terwilliger, Thomas C., Ralf W. Grosse-Kunstleve, Pavel V. Afonine, et al. "Iterative-build OMIT maps: map improvement by iterative model building and refinement without model bias." Acta Crystallographica Section D Biological Crystallography 64, no. 5 (2008): 515–24. http://dx.doi.org/10.1107/s0907444908004319.

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25

Su, Haiyan, Pengzhan Huang, Juan Wen, and Xinlong Feng. "Three Iterative Finite Element Methods for the Stationary Smagorinsky Model." East Asian Journal on Applied Mathematics 4, no. 2 (2014): 132–51. http://dx.doi.org/10.4208/eajam.230913.120314a.

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AbstractThree iterative stabilised finite element methods based on local Gauss integration are proposed in order to solve the steady two-dimensional Smagorinsky model numerically. The Stokes iterative scheme, the Newton iterative scheme and the Oseen iterative scheme are adopted successively to deal with the nonlinear terms involved. Numerical experiments are carried out to demonstrate their effectiveness. Furthermore, the effect of the parameters Re (the Reynolds number) and δ (the spatial filter radius) on the performance of the iterative numerical results is discussed.

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26

Lamouret, I., V. Cornilleau-Pérès, and J. Droulez. "A Computational Model of the Perceived Velocity of Moving Plaids." Perception 25, no. 1_suppl (1996): 87. http://dx.doi.org/10.1068/v96l0712.

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Local motion detection mechanisms generally lead to one component of the optic flow becoming indeterminate. One way to solve this ‘aperture problem’ is to compute the optic flow which minimises some smoothing constraint. With iterative schemes the computed velocity array is suboptimal relative to the constraint until the process has converged. Under the original assumption that the iteration rate is sufficiently low to allow the perception of suboptimal flows at short stimulus durations, iterative gradient models give an accurate description of biases in the perception of tilted line velocity.

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27

Wang, Rongrong, Yangchun Wei, and Ronghu Chi. "Enhanced data-driven optimal iterative learning control for nonlinear non-affine discrete-time systems with iterative sliding-mode surface." Transactions of the Institute of Measurement and Control 42, no. 11 (2020): 1923–34. http://dx.doi.org/10.1177/0142331219900593.

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In this work, an enhanced data-driven optimal iterative learning control (eDDOILC) is proposed for nonlinear nonaffine systems where a new iterative sliding mode surface (ISMS) is designed to replace the traditional tracking error in the controller design and analysis. It is the first time to extend the sliding mode surface to the iteration domain for systems operate repetitively over a finite time interval. By virtual of the new designed ISMS, the control design becomes more flexible where both the time and the iteration dynamics can be taken into account. Before proceeding to the controller

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28

Wei, Yaxin, and Pengzhan Huang. "Finite Element Iterative Methods for the Stationary Double-Diffusive Natural Convection Model." Entropy 24, no. 2 (2022): 236. http://dx.doi.org/10.3390/e24020236.

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In this paper, we consider the stationary double-diffusive natural convection model, which can model heat and mass transfer phenomena. Based on the fixed point theorem, the existence and uniqueness of the considered model are proved. Moreover, we design three finite element iterative methods for the considered problem. Under the uniqueness condition of a weak solution, iterative method I is stable. Compared with iterative method I, iterative method II is stable with a stronger condition. Moreover, iterative method III is stable with the strongest condition. From the perspective of viscosity, i

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29

Wang, Lili, Hexiang Lv, Deyun Chen, Hailu Yang, and Mingyu Li. "Image Reconstruction Algorithm Based on Total Least Squares Target Correction for ECT." Computational Intelligence and Neuroscience 2021 (September 6, 2021): 1–8. http://dx.doi.org/10.1155/2021/3766877.

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In the image reconstruction of the electrical capacitance tomography (ECT) system, the application of the total least squares theory transforms the ill-posed problem into a nonlinear unconstrained minimization problem, which avoids calculating the matrix inversion. But in the iterative process of the coefficient matrix, the ill-posed problem is also produced. For the effect on the final image reconstruction accuracy of this problem, combined with the principle of the ECT system, the coefficient matrix is targeted and updated in the overall least squares iteration process. The new coefficient m

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30

Chien, Chiang-Ju, and Ying-Chung Wang. "A Fuzzy System Based Iterative Learning Control for Nonlinear Discrete-Time Systems with Iteration-Varying Uncertainties." Processes 10, no. 7 (2022): 1275. http://dx.doi.org/10.3390/pr10071275.

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In this paper, we consider an iterative learning control problem for a class of unknown discrete-time nonlinear systems with iteration-varying initial error, iteration-varying system parameters, iteration-varying external disturbance, iteration-varying desired output, and iteration-varying control direction. These iteration-varying uncertainties are not required to take any particular structure such as the high-order internal model and only need to satisfy certain boundedness conditions. We propose an iterative learning control law with an adaptive iteration-varying fuzzy system to overcome al

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31

Roitman, N. "Structural model adjustment using iterative methods." Materials and Structures 36, no. 263 (2003): 570–77. http://dx.doi.org/10.1617/13904.

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32

Flachaire, Emmanuel, and Guillaume Hollard. "Model Selection in Iterative Valuation Questions." Revue d'économie politique 117, no. 5 (2007): 853. http://dx.doi.org/10.3917/redp.175.0853.

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33

Yasukevich, A. S., V. E. Kisel, and A. I. Lazarchuk. "Iterative model for CW fiber lasers." Optical Fiber Technology 88 (December 2024): 104029. http://dx.doi.org/10.1016/j.yofte.2024.104029.

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34

Pouchet, Louis-Noël, Cédric Bastoul, Albert Cohen, and John Cavazos. "Iterative optimization in the polyhedral model." ACM SIGPLAN Notices 43, no. 6 (2008): 90–100. http://dx.doi.org/10.1145/1379022.1375594.

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35

Himpe, Willem, Ruben Corthout, and M. J. Chris Tampère. "An efficient iterative link transmission model." Transportation Research Part B: Methodological 92 (October 2016): 170–90. http://dx.doi.org/10.1016/j.trb.2015.12.013.

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36

Plefka, Jan C. "Iterative solution of the supereigenvalue model." Nuclear Physics B 444, no. 1-2 (1995): 333–52. http://dx.doi.org/10.1016/0550-3213(95)00144-h.

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37

Lu, Jingyi, Zhixing Cao, and Furong Gao. "Multipoint Iterative Learning Model Predictive Control." IEEE Transactions on Industrial Electronics 66, no. 8 (2019): 6230–40. http://dx.doi.org/10.1109/tie.2018.2873133.

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38

Ydstie, B. Erik, and Bo Wahlberg. "Iterative Refinement of Model Predictive Control." IFAC Proceedings Volumes 27, no. 2 (1994): 215–19. http://dx.doi.org/10.1016/s1474-6670(17)48154-7.

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39

Smith, S. W. "ITERATIVE MATRIX APPROXIMATION FOR MODEL UPDATING." Mechanical Systems and Signal Processing 12, no. 1 (1998): 187–201. http://dx.doi.org/10.1006/mssp.1997.0140.

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40

Roitman, N., C. Magluta, and R. S. Oliveira. "Structural model adjustment using iterative methods." Materials and Structures 36, no. 9 (2003): 570–77. http://dx.doi.org/10.1007/bf02483275.

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41

Leu, Sou-Sen, Theresia Daisy Nattali Suparman, and Cathy Chang-Wei Hung. "An Exploratory Study on Optimal Iterative Design Schedules with the Consideration of Design Quality and Resource Constraints." Sustainability 13, no. 8 (2021): 4584. http://dx.doi.org/10.3390/su13084584.

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The classical dependency structure matrix (DSM) can effectively deal with iterative schedules that are highly coupled and interdependent, such as the design process and the concurrent process. Classical DSM generally follows the assumption that the least iteration occurs to achieve the shortest completion time. Nevertheless, the assumption may not hold because tasks ought to be re-visited several times if the design qualities do not meet the requirements. This research proposed a novel iterative scheduling model that combines the classical DSM concept with quality equations. The quality equati

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42

Sun, Zhen, and Zilong Zou. "Towards an efficient method of predicting vehicle-induced response of bridge." Engineering Computations 33, no. 7 (2016): 2067–89. http://dx.doi.org/10.1108/ec-02-2015-0034.

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Purpose The purpose of this paper is to present a practical and efficient iterative method for predicting vehicle-induced response of bridge. Design/methodology/approach The vehicle-bridge interaction (VBI) problem is generalized mathematically and a computational algorithm for VBI is proposed. This method rests on an iterative procedure, which utilizes the whole interaction process for iteration. By this means, vehicle and bridge become totally uncoupled and are only linked by the contact force history. This method provides flexibility to choose simplified or refined vehicle and bridge models

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43

Li, Heng Jie, Xiao Hong Hao, and Xi Ping Pei. "A Nonlinear Optimal Iterative Learning Control Algorithm Based on RBF Neural Network and Clonal Selection Algorithm." Advanced Materials Research 753-755 (August 2013): 1225–29. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.1225.

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Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration; another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking

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44

Wu, Liqing, Naijie Qi, Chengcheng Chang, et al. "Linear mathematical model for the unique solution of 3D ptychographic iterative engine." Chinese Optics Letters 22, no. 5 (2024): 050501. http://dx.doi.org/10.3788/col202422.050501.

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45

LAN, CHIU-YA, HUEY-ER LIN, and SHIH-HSIEN YU. "THE GREEN'S FUNCTION FOR THE BROADWELL MODEL WITH A TRANSONIC BOUNDARY." Journal of Hyperbolic Differential Equations 05, no. 02 (2008): 279–94. http://dx.doi.org/10.1142/s0219891608001489.

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We study an initial boundary value problem for the Broadwell model with a transonic physical boundary. The Green's function for the initial boundary value problem is obtained by combining the estimates of the full boundary data and the Green's function for the initial value problem. The full boundary data is constructed from the imposed boundary data through an iteration scheme. The iteration scheme is designed to separate the interaction between the boundary wave and the interior wave and leads to a convergent series in the iterative boundary estimates.

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46

Xi, Wanqiang, Yaoyao Wang, Bai Chen, and Hongtao Wu. "Iterative learning control of robot based on artificial bee colony algorithm." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, no. 9 (2019): 1221–38. http://dx.doi.org/10.1177/0959651818824202.

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For the repetitive motion control, inaccurate model, and other issues of industrial robots, this article presents a novel control method that the proportion differentiation-type iterative learning parameters are self-tuning based on artificial bee colony algorithm. Considering the influence of the numerical value of iterative learning parameters on the control system, especially in the early iteration, the control effect is not satisfactory. Thus, the artificial bee colony algorithm is introduced in this article. Using bee colony as search unit, the parameters in iterative learning are optimiz

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Oh, Sang–Rok, Zeungnam Bien, and Il Hong Suh. "A model algorithmic learning method for continuous-path control of a robot manipulator." Robotica 8, no. 1 (1990): 31–36. http://dx.doi.org/10.1017/s0263574700007293.

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SUMMARYA new type of an iterative learning control method is proposed for dynamic systems with uncertain parameters. The method, which employs the model algorithmic control concept in the iteration sequence, is shown to be convergent for linear time-varying systems. Then the method is shown to be applicable for continuous-path control of a robot manipulator.

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Maher, Gabriel. "LLMPC: Large Language Model Predictive Control." Computers 14, no. 3 (2025): 104. https://doi.org/10.3390/computers14030104.

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Recent advancements in planning prompting techniques for Large Language Models have improved their reasoning, planning, and action abilities. This paper develops a planning framework for Large Language Models using model predictive control that enables them to iteratively solve complex problems with long horizons. We show that in the model predictive control formulation, LLM planners act as approximate cost function optimizers and solve complex problems by breaking them down into smaller iterative steps. With our proposed planning framework, we demonstrate improved performance over few-shot pr

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49

Akhmetbaev, Dauren, Abdygali Dzhandigulov, Arman Akhmetbaev, Svetlana Bystrova, and Shakizat Begezhanova. "Improvement of Algorithms of the Topological Model of the Steady Mode of Electric Power Systems." E3S Web of Conferences 384 (2023): 01004. http://dx.doi.org/10.1051/e3sconf/202338401004.

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The work improves the algorithms for the formation of a steady state mode of electric power systems, developed on the basis of a topological model. The topological model is developed on the basis of the theory of directed graphs using a matrix of current distribution coefficients. Algorithms for the formation of a steady state provide for conducting calculations with respect to all independent nodes of a complex network of a power system, which leads to an increase in the number of operations of iterative processes. The paper proposes a transformation of the topological model of the system, in

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50

Moreno, Javier. "Iterative differential Galois theory in positive characteristic: A model theoretic approach." Journal of Symbolic Logic 76, no. 1 (2011): 125–42. http://dx.doi.org/10.2178/jsl/1294170992.

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AbstractThis paper introduces a natural extension of Kolchin's differential Galois theory to positive characteristic iterative differential fields, generalizing to the non-linear case the iterative Picard–Vessiot theory recently developed by Matzat and van der Put. We use the methods and framework provided by the model theory of iterative differential fields. We offer a definition of strongly normal extension of iterative differential fields, and then prove that these extensions have good Galois theory and that a G-primitive element theorem holds. In addition, making use of the basic theory of

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