Gotowa bibliografia na temat „Dynamic optimal learning rate”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Dynamic optimal learning rate”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Dynamic optimal learning rate"
Chinrungrueng, C., i C. H. Sequin. "Optimal adaptive k-means algorithm with dynamic adjustment of learning rate". IEEE Transactions on Neural Networks 6, nr 1 (1995): 157–69. http://dx.doi.org/10.1109/72.363440.
Pełny tekst źródłaZhu, Yingqiu, Danyang Huang, Yuan Gao, Rui Wu, Yu Chen, Bo Zhang i Hansheng Wang. "Automatic, dynamic, and nearly optimal learning rate specification via local quadratic approximation". Neural Networks 141 (wrzesień 2021): 11–29. http://dx.doi.org/10.1016/j.neunet.2021.03.025.
Pełny tekst źródłaLeen, Todd K., Bernhard Schottky i David Saad. "Optimal asymptotic learning rate: Macroscopic versus microscopic dynamics". Physical Review E 59, nr 1 (1.01.1999): 985–91. http://dx.doi.org/10.1103/physreve.59.985.
Pełny tekst źródłaKalvit, Anand, i Assaf Zeevi. "Dynamic Learning in Large Matching Markets". ACM SIGMETRICS Performance Evaluation Review 50, nr 2 (30.08.2022): 18–20. http://dx.doi.org/10.1145/3561074.3561081.
Pełny tekst źródłaZheng, Jiangbo, Yanhong Gan, Ying Liang, Qingqing Jiang i Jiatai Chang. "Joint Strategy of Dynamic Ordering and Pricing for Competing Perishables with Q-Learning Algorithm". Wireless Communications and Mobile Computing 2021 (13.03.2021): 1–19. http://dx.doi.org/10.1155/2021/6643195.
Pełny tekst źródłaChen, Zhigang, Rongwei Xu i Yongxi Yi. "Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation". Complexity 2020 (9.06.2020): 1–17. http://dx.doi.org/10.1155/2020/3763684.
Pełny tekst źródłaDe, Shipra, i Darryl A. Seale. "Dynamic Decision Making and Race Games". ISRN Operations Research 2013 (7.08.2013): 1–15. http://dx.doi.org/10.1155/2013/452162.
Pełny tekst źródłaYao, Yuhang, i Carlee Joe-Wong. "Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 5 (18.05.2021): 4608–16. http://dx.doi.org/10.1609/aaai.v35i5.16590.
Pełny tekst źródłaLiu, Haijun. "A Study of an IT-Assisted Higher Education Model Based on Distributed Hardware-Assisted Tracking Intervention". Occupational Therapy International 2022 (8.04.2022): 1–12. http://dx.doi.org/10.1155/2022/8862716.
Pełny tekst źródłaLi, Ao, Zhaoman Wan i Zhong Wan. "Optimal Design of Online Sequential Buy-Price Auctions with Consumer Valuation Learning". Asia-Pacific Journal of Operational Research 37, nr 03 (29.04.2020): 2050012. http://dx.doi.org/10.1142/s0217595920500128.
Pełny tekst źródłaRozprawy doktorskie na temat "Dynamic optimal learning rate"
Cheng, Martin Chun-Sheng, i pjcheng@ozemail com au. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm". Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030722.172812.
Pełny tekst źródłaCheng, Martin Chun-Sheng. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366350.
Pełny tekst źródłaThesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
Full Text
Chang, Yusun. "Dynamic Optimal Fragmentation with Rate Adaptation in Wireless Mobile Networks". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19824.
Pełny tekst źródłaMoncur, Tyler. "Optimal Learning Rates for Neural Networks". BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8662.
Pełny tekst źródłaShu, Weihuan. "Optimal sampling rate assignment with dynamic route selection for real-time wireless sensor networks". Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32351.
Pełny tekst źródłaL'attribution de calcul et de la communication ressources d'une mani`ere qui optimise les performances du syst`eme global est un aspect crucial de la gestion du syst`eme. R´eseau de capteurs sans fil pose de nouveaux d´efis en raison de la p´enurie de ressources et en temps r´eel. Travaux existants a traite distribution temps-reel probl`eme de taux d'´echantillonnage, dans un seul processeur cas et r´eseau cas de routage environment statique. Pour les r´eseaux de capteurs sans fil, afin de parvenir `a une meilleure performance globale du r´eseau, le routage devrait tre examin´e en mˆeme temps que la distribution de taux des flux individuels. Dans cet article, nous abordons le probl`eme de l'optimisation des taux d'´echantillonnage avec route s´election dynamique pour r´eseaux de capteurs sans fil. Nous modelisons le probleme comme un probl`eme d'optimisation et le r´esolvons dans le cadre de l'utilite de reseau maximisation. Sur la base de la m´ethode primal-dual et la dual d´ecomposition technique, nous concevons un algorithme distribu´e qui atteint le meilleur l'utilite de reseau globale au vu de route d´ecision dynamique et le taux distribution. Des simulations ont ´et´e r´ealis´ees pour d´emontrer l'efficience et l'efficacit´e de nos solutions propos´ees. fr
Aroh, Kosisochukwu C. "Determination of optimal conditions and kinetic rate parameters in continuous flow systems with dynamic inputs". Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121815.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 171-185).
.The fourth industrial revolution is said to be brought about by digitization in the manufacturing sector. According to this understanding, the third industrial revolution which involved computers and automation will be further enhanced with smart and autonomous systems fueled by data and machine learning. At the research stage, an analogous story is being told in how automation and new technologies could revolutionize a chemistry laboratory. Flow chemistry is a technique that contrast with traditional batch chemistry in one aspect as a method that facilitates process automation and in small scales, delivers process improvements such as high heat and mass transfer rates. In addition to flow chemistry, analytical tools have also greatly improved and have become fully automated with potential for remote control. Over the past decade, work utilizing optimization techniques to find optimal conditions in flow chemistry have become more prevalent.
In addition, the scope of reactions performed in these systems have also increased. In the first part of this thesis, the construction of a platform capable of performing a wide range of these reactions on the lab scale is discussed. This platform was built with the capability of performing global optimizations using steady state experiments. The rest of the thesis concerns generating dynamic experiments in flow systems and using these conditions to gain more information about a reaction. The ability to use dynamic experiments to accurately determine reaction kinetics is first detailed. Through these experiments we found that only two orthogonal experiments were needed to sample the experimental space. After this an algorithm that utilizes dynamic experiments for kinetic parameter estimation problems is described. The approach here was to use dynamic experiments to first quickly sample the design space to get a reasonable estimate of the kinetic parameters.
Then steady state optimal design of experiments were used to fine tune these estimates. We observed that after initial orthogonal experiments only three more conditions were needed for accurate estimates of the multi-step reaction example. In a similar fashion, an algorithm for reaction optimization that relies on dynamic experiments is also described. The approach here extended that of adaptive response surface methodology where dynamic orthogonal experiments were performed in place of steady state experiments. When compared to steady state optimizations of multi-step reactions, a reduction by half in time needed to locate the optimum is observed. Finally, the potential issues that arise when using transient experiments in automated systems for reaction analysis are addressed. These issues include dispersion, sampling rate, reactor sizes and the rate of change of transients.
These results demonstrate a way with which technological innovation could further revolutionize the chemistry laboratory. By combining machine learning, clouding computing and efficient, high information experiments reaction data could be quickly collected, and the information gained could be maximized for future predictions or optimizations.
by Kosisochukwu C. Aroh.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Chemical Engineering
Ouyang, Hua. "Optimal stochastic and distributed algorithms for machine learning". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49091.
Pełny tekst źródłaLee, Jong Min. "A Study on Architecture, Algorithms, and Applications of Approximate Dynamic Programming Based Approach to Optimal Control". Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5048.
Pełny tekst źródłaBountourelis, Theologos. "Efficient pac-learning for episodic tasks with acyclic state spaces and the optimal node visitation problem in acyclic stochastic digaphs". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/28144.
Pełny tekst źródłaCommittee Chair: Reveliotis, Spyros; Committee Member: Ayhan, Hayriye; Committee Member: Goldsman, Dave; Committee Member: Shamma, Jeff; Committee Member: Zwart, Bert.
Singh, Manish Kumar. "Optimization, Learning, and Control for Energy Networks". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104064.
Pełny tekst źródłaDoctor of Philosophy
Massive infrastructure networks play a pivotal role in everyday human lives. A minor service disruption occurring locally in electric power, natural gas, or water networks is considered a significant loss. Uncertain demands, equipment failures, regulatory stipulations, and most importantly complicated physical laws render managing these networks an arduous task. Oftentimes, the first principle mathematical models for these networks are well known. Nevertheless, the computations needed in real-time to make spontaneous decisions frequently surpass the available resources. Explicitly identifying such problems, this dissertation extends the state of the art on three fronts: First, efficient models enabling the operators to tractably solve some routinely encountered problems are developed using fundamental and diverse mathematical tools; Second, quickly trainable machine learning based solutions are developed that enable spontaneous decision making while learning offline from sophisticated mathematical programs; and Third, control mechanisms are designed that ensure a safe and autonomous network operation without human intervention. These novel solutions are bolstered by mathematical guarantees and extensive simulations on benchmark power, water, and natural gas networks.
Książki na temat "Dynamic optimal learning rate"
Chater, Sean Christopher. The optimal rate of monetary growth in a dynamic macro model with a labour market distortion. [s.l.]: typescript, 1995.
Znajdź pełny tekst źródłaLin, Xiaofeng, Qinglai Wei, Ruizhuo Song i Benkai Li. Self-Learning Optimal Control of Nonlinear Systems: Adaptive Dynamic Programming Approach. Springer, 2017.
Znajdź pełny tekst źródłaLin, Xiaofeng, Qinglai Wei, Ruizhuo Song i Benkai Li. Self-Learning Optimal Control of Nonlinear Systems: Adaptive Dynamic Programming Approach. Springer, 2019.
Znajdź pełny tekst źródłaHorneff, Vanya, Raimond Maurer i Olivia S. Mitchell. How Persistent Low Expected Returns Alter Optimal Life Cycle Saving, Investment, and Retirement Behavior. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198827443.003.0008.
Pełny tekst źródłaBack, Kerry E. Real Options and q Theory. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0020.
Pełny tekst źródłaHughes, Jim. Hip and femur. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198813170.003.0013.
Pełny tekst źródłaBjörk, Tomas. Arbitrage Theory in Continuous Time. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198851615.001.0001.
Pełny tekst źródłaVervaeke, John, Leo Ferraro i Arianne Herrera-Bennett. Flow as Spontaneous Thought. Redaktorzy Kalina Christoff i Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.8.
Pełny tekst źródłaCzęści książek na temat "Dynamic optimal learning rate"
Powell, Warren B., i Ilya O. Ryzhov. "Optimal Learning and Approximate Dynamic Programming". W Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, 410–31. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118453988.ch18.
Pełny tekst źródłaKim, Minjung, i Yucel Altunbasak. "Optimal Dynamic Rate Shaping for Compressed Video Streaming". W Networking — ICN 2001, 786–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47734-9_78.
Pełny tekst źródłaWei, Qinglai, Ruizhuo Song, Benkai Li i Xiaofeng Lin. "Principle of Adaptive Dynamic Programming". W Self-Learning Optimal Control of Nonlinear Systems, 1–17. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4080-1_1.
Pełny tekst źródłaDvijotham, K., i E. Todorov. "Linearly Solvable Optimal Control". W Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, 119–41. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118453988.ch6.
Pełny tekst źródłaYou, Shuai, Wanyi Gao, Ziyang Li, Qifen Yang, Meng Tian i Shuhua Zhu. "Dynamic Adjustment of the Learning Rate Using Gradient". W Human Centered Computing, 61–69. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23741-6_6.
Pełny tekst źródłaKuflik, Tsvi, Bracha Shapira, Yuval Elovici i Adlai Maschiach. "Privacy Preservation Improvement by Learning Optimal Profile Generation Rate". W User Modeling 2003, 168–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44963-9_23.
Pełny tekst źródłaFairbank, Michael, Danil Prokhorov i Eduardo Alonso. "Approximating Optimal Control with Value Gradient Learning". W Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, 142–61. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118453988.ch7.
Pełny tekst źródłaVamvoudakis, Kyriakos G., i Frank L. Lewis. "Online Learning Algorithms for Optimal Control and Dynamic Games". W Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, 350–77. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118453988.ch16.
Pełny tekst źródłaLiu, Derong, Qinglai Wei, Ding Wang, Xiong Yang i Hongliang Li. "Learning Algorithms for Differential Games of Continuous-Time Systems". W Adaptive Dynamic Programming with Applications in Optimal Control, 417–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50815-3_11.
Pełny tekst źródłaRafajłowicz, Ewaryst, i Wojciech Rafajłowicz. "Iterative Learning in Repetitive Optimal Control of Linear Dynamic Processes". W Artificial Intelligence and Soft Computing, 705–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39378-0_60.
Pełny tekst źródłaStreszczenia konferencji na temat "Dynamic optimal learning rate"
Roy, Serge. "Near-optimal dynamic learning rate for training backpropagation neural networks". W Optical Engineering and Photonics in Aerospace Sensing, redaktor Dennis W. Ruck. SPIE, 1993. http://dx.doi.org/10.1117/12.152627.
Pełny tekst źródłaZhang, Tong, C. L. Philip Chen, Chi-Hsu Wang i Sik Chung Tam. "A new dynamic optimal learning rate for a two-layer neural network". W 2012 International Conference on System Science and Engineering (ICSSE). IEEE, 2012. http://dx.doi.org/10.1109/icsse.2012.6257148.
Pełny tekst źródłaTong, Zhang, C. L. Philip Chen i Zhou Jin. "Impact of ratio k on two-layer neural networks with dynamic optimal learning rate". W 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889774.
Pełny tekst źródłaViaña Perez, Javier, Drew Scott, Manish Kumar i Kelly Cohen. "Dynamic Genetic Algorithm for Optimizing Movement of a Six-Limb Creature". W ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3243.
Pełny tekst źródłaCao, Jie, Hui Ren i Dan Sui. "Global Optimization Workflow for Offshore Drilling Rate of Penetration With Dynamic Drilling Log Data". W ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-79747.
Pełny tekst źródłaTrogmann, Hannes, Harald Waschl, Daniel Alberer i Bernhard Spiegl. "Time Optimal Compressor Valve Soft Landing by Two Step ILC". W ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6199.
Pełny tekst źródłaAmadi, Kingsley Williams, Ibiye Iyalla, Prabhua Radhakrishna, Mortadha Torki Al Saba i Marwa Mustapha Waly. "Continuous Dynamic Drill-Off Test Whilst Drilling Using Reinforcement Learning in Autonomous Rotary Drilling System". W ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211723-ms.
Pełny tekst źródłaNarayan, Meenakshi, i Ann Majewicz Fey. "A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework". W ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3329.
Pełny tekst źródłaDocherty, David, Dale Erickson i Scott Henderson. "Using Ai to Optimize the Use of Gas Lift in Oil Wells". W ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211028-ms.
Pełny tekst źródłaFang, Haowen, Amar Shrestha, Ziyi Zhao i Qinru Qiu. "Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/388.
Pełny tekst źródłaRaporty organizacyjne na temat "Dynamic optimal learning rate"
Araya, Mesele, Caine Rolleston, Pauline Rose, Ricardo Sabates, Dawit Tibebu Tiruneh i Tassew Woldehanna. Understanding the Impact of Large-Scale Educational Reform on Students’ Learning Outcomes in Ethiopia: The GEQIP-II Case. Research on Improving Systems of Education (RISE), styczeń 2023. http://dx.doi.org/10.35489/bsg-rise-wp_2023/125.
Pełny tekst źródła