Готові списки джерел за темами / Learning based control

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  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Learning based control"

Автор: Grafiati
Опубліковано: 3 червня 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "Learning based control"

1

Hewing, Lukas, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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Анотація:
Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC
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2

Jiménez, E., and M. Rodríguez. "WEB BASED PROCESS CONTROL LEARNING." IFAC Proceedings Volumes 39, no. 6 (2006): 349–54. http://dx.doi.org/10.3182/20060621-3-es-2905.00061.

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3

Berti, Dyah Permatasari, Gunarhadi, and Riyadi. "The influence of problem based learning towards social science learning outcomes viewed from learning interest." International Journal of Evaluation and Research in Education (IJERE) 8, no. 1 (2019): 39–46. https://doi.org/10.11591/ijere.v8i1.15594.

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The aim of this study is to determine the influences of Problem Based Learning and learning interest at improving the cognitive learning outcomes in social science of fourth-grade elementary school students. This study is a quasi-experiment study with pre-test and post-test control group design. The sample collection is taken with stratified cluster random sampling technique. The sample of this study is 109 fourth-grade elementary school students, consisted of 50 students from experiment group and 59 students from control group. The students from experiment group are given the application of P
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4

Hadi Saputro, Suyatno. "The Impact of Problem Based Learning on Learning Outcomes in Nursing Students." International Journal of Multidisciplinary Research and Analysis 05, no. 10 (2022): 2784–88. http://dx.doi.org/10.47191/ijmra/v5-i10-29.

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This study aims to determine the impact of problem-based learning on nursing student learning outcomes. The research method used is quasi-experimental by comparing the two groups, namely the group treated with problem-based learning and conventional learning. The research subjects were 7th semester nursing students, totaling 68 students who were divided into two groups, experiment with problem-based learning and control with conventional learning. Analysis of the data in this study by comparing learning outcomes in the two groups, experimental and control. The results of data analysis showed t
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5

Hermawan, Edy, I. Putu Eka Indrawan, and Ni Luh Lianjayani. "Pengaruh Model Pembelajaran Problem Based Learning Berbantuan E-Learning terhadap Kreativitas dan Hasil Belajar Matematika." Emasains 7, no. 2 (2018): 170–77. https://doi.org/10.5281/zenodo.2548093.

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This study aimed to assess the effect of Problem Based Learning model with e-learning to creativity and learning result of mathematics on tenth grade student of SMK Negeri 4 Denpasar on academic year 2017/2018. This type of research was quasi experiment, using non equivalent posttest-only control group design. The population in this study were the student of tenth grade PH of SMK Negeri 4 Denpasar with 4 classes (144 students). Using multistage random sampling technique, 2 classes were obtained as sample, wich is tenth grade PH 3 (36 students) as control group and tenth grade PH 1 (36 students
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6

Chiuso, A., and G. Pillonetto. "System Identification: A Machine Learning Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 281–304. http://dx.doi.org/10.1146/annurev-control-053018-023744.

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Анотація:
Estimation of functions from sparse and noisy data is a central theme in machine learning. In the last few years, many algorithms have been developed that exploit Tikhonov regularization theory and reproducing kernel Hilbert spaces. These are the so-called kernel-based methods, which include powerful approaches like regularization networks, support vector machines, and Gaussian regression. Recently, these techniques have also gained popularity in the system identification community. In both linear and nonlinear settings, kernels that incorporate information on dynamic systems, such as the smoo
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Suyatno, Hadi Saputro, Mustaji, and Arianto Fajar. "The Impact of Problem Based Learning on Learning Outcomes in Nursing Students." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 05, no. 10 (2022): 2784–88. https://doi.org/10.5281/zenodo.7214984.

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Анотація:
This study aims to determine the impact of problem-based learning on nursing student learning outcomes. The research method used is quasi-experimental by comparing the two groups, namely the group treated with problem-based learning and conventional learning. The research subjects were 7th semester nursing students, totaling 68 students who were divided into two groups, experiment with problem-based learning and control with conventional learning. Analysis of the data in this study by comparing learning outcomes in the two groups, experimental and control. The results of data analysis showed t
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Songjun, M., and D. H. Owens. "POLYNOMIALS BASED ITERATIVE LEARNING CONTROL ALGORITHM." IFAC Proceedings Volumes 40, no. 18 (2007): 623–29. http://dx.doi.org/10.3182/20070927-4-ro-3905.00103.

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Mirwald, Jonas, Johannes Ultsch, Ricardo de Castro, and Jonathan Brembeck. "Learning-Based Cooperative Adaptive Cruise Control." Actuators 10, no. 11 (2021): 286. http://dx.doi.org/10.3390/act10110286.

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Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were
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Xiong, Xiaofeng, Cao Danh Do, and Poramate Manoonpong. "Learning-Based Multifunctional Elbow Exoskeleton Control." IEEE Transactions on Industrial Electronics 69, no. 9 (2022): 9216–24. http://dx.doi.org/10.1109/tie.2021.3116572.

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Дисертації з теми "Learning based control"

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Wallén, Johanna. "Estimation-based iterative learning control." Doctoral thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-64017.

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Анотація:
In many  applications industrial robots perform the same motion  repeatedly. One way of compensating the repetitive part of the error  is by using iterative learning control (ILC). The ILC algorithm  makes use of the measured errors and iteratively calculates a  correction signal that is applied to the system. The main topic of the thesis is to apply an ILC algorithm to a  dynamic system where the controlled variable is not measured. A  remedy for handling this difficulty is to use additional sensors in  combination with signal processing algorithms to obtain estimates of  the controlled varia
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2

Hosseinkhan-Boucher, Rémy. "On Learning-Based Control of Dynamical Systems." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG029.

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Анотація:
Les impératifs environnementaux suscitent un regain d’intérêt pour la recherche sur le contrôle de l’écoulement des fluides afin de réduire la consommation d’énergie et les émissions dans diverses applications telles que l’aéronautique et l’automobile. Les stratégies de contrôle des fluides peuvent optimiser le système en temps réel, en tirant parti des mesures des capteurs et des modèles physiques. Ces stratégies visent à manipuler le comportement d’un système pour atteindre un état souhaité (stabilité, performance, consommation d’énergie). Dans le même temps, le développement d’approches de
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3

Desimone, Roberto V. "Learning control knowledge within an explanation-based learning framework." Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/18827.

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Parisi, Aaron Thomas. "An Application of Sliding Mode Control to Model-Based Reinforcement Learning." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2054.

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Анотація:
The state-of-art model-free reinforcement learning algorithms can generate admissible controls for complicated systems with no prior knowledge of the system dynamics, so long as sufficient (oftentimes millions) of samples are available from the environ- ment. On the other hand, model-based reinforcement learning approaches seek to leverage known optimal or robust control to reinforcement learning tasks by mod- elling the system dynamics and applying well established control algorithms to the system model. Sliding-mode controllers are robust to system disturbance and modelling errors, and have
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5

Moore, Andrew William. "Efficient memory-based learning for robot control." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385715.

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6

Krasoulis, Agamemnon. "Machine learning-based dexterous control of hand prostheses." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31213.

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Анотація:
Upper-limb myoelectric prostheses are controlled by muscle activity information recorded on the skin surface using electromyography (EMG). Intuitive prosthetic control can be achieved by deploying statistical and machine learning (ML) tools to decipher the user's movement intent from EMG signals. This thesis proposes various means of advancing the capabilities of non-invasive, ML-based control of myoelectric hand prostheses. Two main directions are explored, namely classification-based hand grip selection and proportional finger position control using regression methods. Several practical aspe
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7

Kebbati, Mohammed-Yassine. "Learning-based reinforced control strategies for autonomous vehicles." Electronic Thesis or Diss., université Paris-Saclay, 2023. https://www.biblio.univ-evry.fr/theses/2023/interne/2023UPAST071.pdf.

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Dans un monde où les gens sont constamment en mouvement et ou la mobilité personnelle est préférée, le nombre de voitures personnelles sur la route ne cesse d'augmenter. Cela a induit plus de risque d'accidents de route, des conditions de circulation et de pollution de l'air plus dégradées. Par conséquent, les chercheurs travaillent depuis des décennies pour une transition vers la conduite autonome. Cette dernière a la capacité de remodeler la mobilité en diminuant les accidents de route, les embouteillages et la pollution de l'air, ce qui se traduirait par plus d'efficacité énergétique et de
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8

Martucci, Mejía Francisco. "IMC-based Iterative Learning Control of a Solar Plant." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-164889.

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9

Taylor, Graham. "Reinforcement Learning for Parameter Control of Image-Based Applications." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/832.

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The significant amount of data contained in digital images present barriers to methods of learning from the information they hold. Noise and the subjectivity of image evaluation further complicate such automated processes. In this thesis, we examine a particular area in which these difficulties are experienced. We attempt to control the parameters of a multi-step algorithm that processes visual information. A framework for approaching the parameter selection problem using reinforcement learning agents is presented as the main contribution of this research. We focus on the generation
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Umemoto, Makiko. "Machine learning-based quality control for air temperature data." Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1439448.

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Книги з теми "Learning based control"

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Zhou, Xuefeng, Zhihao Xu, Shuai Li, Hongmin Wu, Taobo Cheng, and Xiaojing Lv. AI based Robot Safe Learning and Control. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5503-9.

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Zhou, Xuefeng. AI based Robot Safe Learning and Control. Springer Nature, 2020.

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3

Minton, Steven. Learning search control knowledge: An explanation-based approach. Kluwer Academic, 1988.

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4

Minton, Steven. Learning Search Control Knowledge: An Explanation-Based Approach. Springer US, 1988.

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5

United States. National Aeronautics and Space Administration., ed. Learning fuzzy logic control system. National Aeronautics and Space Administration, 1994.

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6

Grzeszczuk, Radek. Automated learning of muscle based locomotion through control abstraction. University of Toronto, Dept. of Computer Science, 1994.

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7

Grzeszczuk, Radek. Automated learning of muscle based locomotion through control abstraction. National Library of Canada, 1993.

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8

Cohen, Max, and Calin Belta. Adaptive and Learning-Based Control of Safety-Critical Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29310-8.

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9

Andrew, Barry P. Distance interactive learning in economics: Computer based learning using remote control software. University of Portsmouth, Dept. of Economics, 1993.

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10

Tran, Tien Anh, Kolla Bhanu Prakash, Subrata Chowdhury, and Ivan CK Tam. Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques. CRC Press, 2023. http://dx.doi.org/10.1201/9781003320074.

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Частини книг з теми "Learning based control"

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Cheng, Hwee Ming. "Respiratory Control." In Physiology Question-Based Learning. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12790-3_10.

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2

Parkins, Eric John. "Learning-based control." In Cerebellum and Cerebrum in Homeostatic Control and Cognition. Routledge, 2021. http://dx.doi.org/10.4324/9781003024606-4.

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3

Fürnkranz, Johannes, Philip K. Chan, Susan Craw, et al. "Model-Based Control." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_555.

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4

Choi, Jin Young, Chong-Ho Choi, and Hyun Joo Park. "Neural-Based Iterative Learning Control." In Iterative Learning Control. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5629-9_12.

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5

Gosavi, Abhijit. "Control Optimization with Learning Automata." In Simulation-Based Optimization. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3766-0_10.

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6

Gosavi, Abhijit. "Control Optimization with Reinforcement Learning." In Simulation-Based Optimization. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3766-0_9.

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Gosavi, Abhijit. "Control Optimization with Reinforcement Learning." In Simulation-Based Optimization. Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7491-4_7.

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Owens, David H. "Combined Inverse and Gradient Based Design." In Iterative Learning Control. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6772-3_8.

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9

Aykent, Baris. "Reinforcement Learning-Based Control." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85689-1_6.

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Lee, Kwang Soon, and Jay H. Lee. "Design of Quadratic Criterion-Based Iterative Learning Control." In Iterative Learning Control. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5629-9_9.

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Тези доповідей конференцій з теми "Learning based control"

1

Sheng, Liu Tian, Afizan Bin Azman, Navid Ali Khan, Siva Raja Sindiramutty, Suresh Singh, and Sahil Verma. "Reinforcement learning-based traffic signal control." In 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC). IEEE, 2024. https://doi.org/10.1109/etncc63262.2024.10767501.

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2

Kim, Ye Rin, and Hyun Duck Choi. "CNN-based Apprenticeship Learning for Inverse Reinforcement Learning." In 2024 24th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773173.

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3

Chen, Xiaoya, Xiangbin Liu, and Min Zhang. "Autonomous Vehicle Lane-Change Control Based on Model Predictive Control with Control Barrier Function." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606562.

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Ananthi, P., K. Nirmaladevi, and Naveen Kumar S. "Intrusion Detection Mechanism Using Deep Learning." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823331.

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Atherton, D. P. "Teaching control engineering using the Control Kit." In IEE Colloquium on `Computer Based Learning in Electronic Education'. IEE, 1995. http://dx.doi.org/10.1049/ic:19950631.

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Yang, Zhile, Kang Li, and Lidong Zhang. "Binary teaching-learning based optimization for power system unit commitment." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737550.

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Rocha, T., S. Paredes, J. Henriques, P. Carvalho, A. Cardoso, and P. Gil. "Adaptive Control Learning Based on a Similarity Measure." In 2018 13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO). IEEE, 2018. http://dx.doi.org/10.1109/controlo.2018.8514277.

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CHAN, CC, REGGIE KWAN, and S. F. CHAN. "WEB BASED LEARNING MODULE: CONTROL SYSTEMS." In Proceedings of the Third International Conference on Web-based Learning (ICWL 2004). WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702494_0005.

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Cui, Rongxin, Chenguang Yang, Yang Li, and Sanjay Sharma. "Neural network based reinforcement learning control of autonomous underwater vehicles with control input saturation." In 2014 UKACC 10th International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915114.

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Hao, Ni, Liu Fanghua, Ruan Ruolin, Hu Yonghong, Wu Aixia, and Mao Caixia. "Image Denoising Based on Online Dictionary Learning." In 2018 13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO). IEEE, 2018. http://dx.doi.org/10.1109/controlo.2018.8439818.

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Звіти організацій з теми "Learning based control"

1

VanLehn, Kurt, and Randolph M. Jones. Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control,. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada240775.

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Karnowski, Thomas, Matthew Eicholtz, Wael Elwasif, et al. Reinforcement Learning-based Traffic Control to Optimize Energy Usage and Throughput (CRADA report). Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1560460.

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Aswani, Anil, Humberto Gonzalez, S. S. Sastry, and Claire Tomlin. Statistical Results on Filtering and Epi-convergence for Learning-Based Model Predictive Control. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada558989.

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Семеріков, Сергій Олексійович, Світлана Миколаївна Амеліна, and Ростислав Олександрович Тарасенко. Enhancing foreign language learning with cloud-based mind mapping techniques. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/8484.

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Анотація:
This paper explores the potential of using cloud-based mind maps as a tool for learning foreign languages. It is concluded that their use is suitable for both language classes and students’ independent work. Criteria have been developed for evaluating cloud services in terms of their effectiveness in the educational process of creating mind maps. The paper characterises the conditions for accessing free versions of 16 cloud services for creating mind maps. Based on an experimental study, five cloud services are compared: Ayoa, Mindomo, Miro, Smartdraw, and Xmind. The paper demonstrates example
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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Sadigh, Dorsa, Eric Kim, Samuel Coogan, S. S. Sastry, and Sanjt A. Seshia. A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada623517.

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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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Aziz, H. M. Abdul, Hong Wang, Stanley Young, and SMA Bin al islam. Investigating the Impact of Connected Vehicle Market Share on the Performance of Reinforcement-Learning Based Traffic Signal Control. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1566974.

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Napolitano, Marcello R. YF22 Model With On-Board On-Line Learning Microprocessors-Based Neural Algorithms for Autopilot and Fault-Tolerant Flight Control Systems. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada400639.

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Berlanga, Cecilia, Emma Näslund-Hadley, Enrique Fernández García, and Juan Manuel Hernández Agramonte. Hybrid parental training to foster play-based early childhood development: experimental evidence from Mexico. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004879.

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Play during early childhood is key to stimulating childrens physical, social, emotional and cognitive development; it promotes their imagination and creativity, improves their problem-solving skills and enhances their learning readiness by providing the foundations to build skills later in their lives. Parental engagement in play-based learning at home is one of the behaviors most consistently associated with positive child development. However, it is concerning that levels of parental engagement in play activities have been found to be lower in low-resourced settings. Additionally, research o
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