Literatura científica selecionada sobre o tema "Predictive programming"
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Artigos de revistas sobre o assunto "Predictive programming"
de Madrid, A. P., S. Dormido, F. Morilla e L. Grau. "Dynamic Programming Predictive Control". IFAC Proceedings Volumes 29, n.º 1 (junho de 1996): 1721–26. http://dx.doi.org/10.1016/s1474-6670(17)57917-3.
Texto completo da fonteKulcsár, Zsuzsanna, János Nagy e Mária Nábrády. "Hemisphericity and predictive motor programming". International Journal of Psychophysiology 11, n.º 1 (julho de 1991): 49. http://dx.doi.org/10.1016/0167-8760(91)90209-g.
Texto completo da fonteXie, Haotian, Jianming Du, Dongliang Ke, Yingjie He, Fengxiang Wang, Christoph Hackl, José Rodríguez e Ralph Kennel. "Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution". Symmetry 14, n.º 3 (21 de março de 2022): 626. http://dx.doi.org/10.3390/sym14030626.
Texto completo da fonteRao, Christopher V., e James B. Rawlings. "Linear programming and model predictive control". Journal of Process Control 10, n.º 2-3 (abril de 2000): 283–89. http://dx.doi.org/10.1016/s0959-1524(99)00034-7.
Texto completo da fonteRodríguez, Arturo, e Joaquín Trigueros. "Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming". Estudios de Administración 15, n.º 2 (4 de fevereiro de 2020): 47. http://dx.doi.org/10.5354/0719-0816.2008.56413.
Texto completo da fonteBabu, Mr M. Jeevan. "Mental Health Prediction Using Catboost Algorithm". International Journal for Research in Applied Science and Engineering Technology 12, n.º 3 (31 de março de 2024): 3449–53. http://dx.doi.org/10.22214/ijraset.2024.59219.
Texto completo da fonteJianhong, Wang. "Dynamic Programming in Data Driven Model Predictive Control?" WSEAS TRANSACTIONS ON SYSTEMS 20 (21 de julho de 2021): 170–77. http://dx.doi.org/10.37394/23202.2021.20.19.
Texto completo da fonteDixon, Kevin R., John M. Dolan e Pradeep K. Khosla. "Predictive Robot Programming: Theoretical and Experimental Analysis". International Journal of Robotics Research 23, n.º 9 (setembro de 2004): 955–73. http://dx.doi.org/10.1177/0278364904044401.
Texto completo da fonteDavidson, Curt, e Alan Ewert. "College Student Commitment and Outdoor Orientation Programming". Journal of Experiential Education 43, n.º 3 (1 de junho de 2020): 299–316. http://dx.doi.org/10.1177/1053825920923709.
Texto completo da fonteOhmori, Shunichi. "A Predictive Prescription Using Minimum Volume k-Nearest Neighbor Enclosing Ellipsoid and Robust Optimization". Mathematics 9, n.º 2 (7 de janeiro de 2021): 119. http://dx.doi.org/10.3390/math9020119.
Texto completo da fonteTeses / dissertações sobre o assunto "Predictive programming"
König, Rikard. "Enhancing genetic programming for predictive modeling". Doctoral thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3689.
Texto completo da fonteAvhandling för teknologie doktorsexamen i datavetenskap, som kommer att försvaras offentligt tisdagen den 11 mars 2014 kl. 13.15, M404, Högskolan i Borås. Opponent: docent Niklas Lavesson, Blekinge Tekniska Högskola, Karlskrona.
Buerger, Johannes Albert. "Fast model predictive control". Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:6e296415-f02c-4bc2-b171-3bee80fc081a.
Texto completo da fonteFreiwat, Sami, e Lukas Öhlund. "Fuel-Efficient Platooning Using Road Grade Preview Information". Thesis, Uppsala universitet, Avdelningen för systemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-270263.
Texto completo da fonteFelipe, Dominguez Luis Felipe Dominguez. "Advances in multiparametric nonlinear programming & explicit model predictive control". Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536023.
Texto completo da fonteRivotti, Pedro. "Multi-parametric programming and explicit model predictive control of hybrid systems". Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24432.
Texto completo da fonteBennett, Andrew David. "Using genetic programming to learn predictive models from spatio-temporal data". Thesis, University of Leeds, 2010. http://etheses.whiterose.ac.uk/1376/.
Texto completo da fonteJonsson, Johan. "Fuel Optimized Predictive Following in Low Speed Conditions". Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1937.
Texto completo da fonteThe situation when driving in dense traffic and at low speeds is called Stop and Go. A controller for automatic following of the car in front could under these conditions reduce the driver's workload and keep a safety distance to the preceding vehicle through different choices of gear and engine torque. The aim of this thesis is to develop such a controller, with an additional focus on lowering the fuel consumption. With help of GPS, 3D-maps and sensors information about the slope of the road and the preceding vehicle can be obtained. Using this information the controller is able to predict future possible control actions and an optimization algorithm can then find the best inputs with respect to some criteria. The control method used is Model Predictive Control (MPC) and as the name indicate a model of the control object is required for the prediction. To find the optimal sequence of inputs, the optimization method Dynamic Programming choose the one which lead to the lowest fuel consumption and satisfactory following. Simulations have been made using a reference trajectory which was measured in a real traffic jam. The simulations show that it is possible to follow the preceding vehicle in a good way and at the same time reduce the fuel consumption with approximately 3 %.
Andersson, Emma. "Intuitive Mission Handling with Automatic Route Re-planning using Model Predictive Control". Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-80638.
Texto completo da fonteSystemet för uppdragshantering i stridsflygplanet Gripen, och i dess markstödsystem, består bland annat av uppdragsplanering, skapande av uppdragspunkter och möjligheter att validera utförda uppdrag. Systemet är komplext och exempelvis växer antalet uppdragspunkter med omvärldens ökande krav och behov. Detta examensarbete presenterar förslag till förenklingar och förbättringar i uppdragshanteringssystemet, för att göra det mer intuitivt och användarvänligt. Som grund för förslagen har intervjuer med piloter från Saab, TUJAS och FMV gjorts, för att samla in åsikter och idéer från de som använder systemet och har bred kunskap om det. En förbättring är en möjlighet till online automatisk omplanering av uppdragsrutten vid hinder. MPC (modellbaserad prediktionsreglering) har använts för att estimera den dynamiska fiendens flygväg, och beräkna en ny rutt till nästa uppdragspunkt som inte ligger i konflikt med den estimerade vägen för hindret. Detta system har implementerats i Matlab och konceptet demonstreras med olika testscenarion där prestandaparametrar (prediktionshorisont och straff i kostnadsfunktionen) för regulatorn varieras, och stationära och rörliga hinder induceras.
AL_Sheakh, Ameen Nael [Verfasser]. "Programming and Industrial Control, Model-Based Predictive Control of 3-Level Inverters / Nael AL_Sheakh Ameen". Wuppertal : Universitätsbibliothek Wuppertal, 2012. http://d-nb.info/1022901303/34.
Texto completo da fonteJonsson, Holm Erik. "Predictive Energy Management of Long-Haul Hybrid Trucks : Using Quadratic Programming and Branch-and-Bound". Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178224.
Texto completo da fonteLivros sobre o assunto "Predictive programming"
author, Mayor Eric, e Forte Rui Miguel author, eds. R: Predictive analysis : master the art of predictive modeling. Birmingham, UK: Packt Publishing, 2017.
Encontre o texto completo da fonteNitin, Indurkhya, e Zhang Tong 1971-, eds. Fundamentals of predictive text mining. London: Springer-Verlag, 2010.
Encontre o texto completo da fonteLearning data mining with Python: Harness the power of Python to analyze data and create insightful predictive models. Birmingham, UK: Packt Publishing, 2015.
Encontre o texto completo da fonteC, Georgiadis Michael, Pistikopoulos Efstratios N e Dua Vivek, eds. Multi-parametric model-based control: Theory and applications. Weinheim: Wiley-VCH, 2007.
Encontre o texto completo da fonteFahringer, Thomas. Automatic performance prediction of parallel programs. Boston: Kluwer Academic Publishers, 1996.
Encontre o texto completo da fonteHyslop, William F. Performance prediction of relational database management systems. Toronto: Computer Systems Research Institute, University of Toronto, 1991.
Encontre o texto completo da fonteFahringer, Thomas. Automatic Performance Prediction of Parallel Programs. Boston, MA: Springer US, 1996.
Encontre o texto completo da fonteR, Horn J., e United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Geometric programming prediction of design trends for OMV protective structures. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.
Encontre o texto completo da fonteRauscher, Harold M. The microcomputer scientific software series 4: Testing prediction accuracy. St. Paul, Minn: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station, 1986.
Encontre o texto completo da fonteBrown, Robert Goodell. Smoothing, forecasting and prediction of discrete time series. Mineola, NY: Dover Publications, 2004.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Predictive programming"
Grancharova, Alexandra, e Tor Arne Johansen. "Multi-parametric Programming". In Explicit Nonlinear Model Predictive Control, 1–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_1.
Texto completo da fonteMathavaraj, S., e Radhakant Padhi. "Model Predictive Static Programming". In Satellite Formation Flying, 111–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9631-5_7.
Texto completo da fonteFerreira, Carlos Abreu, João Gama e Vítor Santos Costa. "Predictive Sequence Miner in ILP Learning". In Inductive Logic Programming, 130–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31951-8_15.
Texto completo da fonteSaerens, Bart, Moritz Diehl e Eric Van den Bulck. "Optimal Control Using Pontryagin’s Maximum Principle and Dynamic Programming". In Automotive Model Predictive Control, 119–38. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_8.
Texto completo da fonteMarathe, Madhav V. "Towards a Predictive Computational Complexity Theory". In Automata, Languages and Programming, 22–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45465-9_2.
Texto completo da fonteKirches, Christian. "Constrained Nonlinear Programming". In Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control, 61–87. Wiesbaden: Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8_4.
Texto completo da fonteZavala, Victor M., e Lorenz T. Biegler. "Nonlinear Programming Strategies for State Estimation and Model Predictive Control". In Nonlinear Model Predictive Control, 419–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_33.
Texto completo da fonteŠourek, Gustav, Suresh Manandhar, Filip Železný, Steven Schockaert e Ondřej Kuželka. "Learning Predictive Categories Using Lifted Relational Neural Networks". In Inductive Logic Programming, 108–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63342-8_9.
Texto completo da fonteGrüne, Lars. "Dynamic Programming, Optimal Control and Model Predictive Control". In Handbook of Model Predictive Control, 29–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77489-3_2.
Texto completo da fonteTamaddoni-Nezhad, Alireza, David Bohan, Alan Raybould e Stephen Muggleton. "Towards Machine Learning of Predictive Models from Ecological Data". In Inductive Logic Programming, 154–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23708-4_11.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Predictive programming"
Dantas, Danilo Medeiros, Jucelio Soares dos Santos, Kézia de Vasconcelos Oliveira Dantas, Wilkerson L. Andrade, João Brunet e Monilly Ramos Araujo Melo. "Screening Programming’s Reliability to Measure Predictive Programming Skills". In Simpósio Brasileiro de Informática na Educação. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbie.2023.235112.
Texto completo da fonteMeadows, E. S. "Dynamic programming and model predictive control". In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.610861.
Texto completo da fonteBeeri, Catriel, e Tova Milo. "Functional and predictive programming in OODB's". In the eleventh ACM SIGACT-SIGMOD-SIGART symposium. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/137097.137863.
Texto completo da fonteNewsom, David K., Sardar F. Azari, Ahmad Anbar e Tarek El-Ghazawi. "Predictive energy management techniques for PGAS programming". In 2013 ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2013. http://dx.doi.org/10.1109/aiccsa.2013.6616462.
Texto completo da fonteMorgenstern, Dimitri, Daniel Gorges e Andreas Wirsen. "Obtaining a Stabilizing Prediction Horizon in Quadratic Programming Model Predictive Control". In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9030254.
Texto completo da fonteChisci, L. "Stabilising predictive control: static vs dynamic programming approach". In UKACC International Conference on Control. Control '96. IEE, 1996. http://dx.doi.org/10.1049/cp:19960752.
Texto completo da fonteEggimann, Marc-Andre, Oscar D. Crisalle e Roland Longchamp. "A Linear-Programming Predictive Controller with Variable Horizon". In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792372.
Texto completo da fonteCui, Hairong, Wei Wang e Xiangjie Liu. "Robust model predictive control based on linear programming". In 2011 2nd International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2011. http://dx.doi.org/10.1109/icicip.2011.6008405.
Texto completo da fonteAmezquita-Brooks, Luis, e Jesus Liceaga-Castro. "A simple non-windup linear programming predictive controller". In Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/cerma.2007.4367668.
Texto completo da fonteCalafiore, G. C., e L. Fagiano. "Robust model predictive control via random convex programming". In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6160548.
Texto completo da fonteRelatórios de organizações sobre o assunto "Predictive programming"
Fogel, Lawrence J., e David Fogel. Artificial Intelligence through Evolutionary Programming: Prediction and Identification. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1986. http://dx.doi.org/10.21236/ada171544.
Texto completo da fonteNeely, Christopher J., e Paul A. Weller. Predicting Exchange Rate Volatility: Genetic Programming vs. GARCH and Risk Metrics™. Federal Reserve Bank of St. Louis, 2001. http://dx.doi.org/10.20955/wp.2001.009.
Texto completo da fonteShaw, Alan C. Specifying, Predicting, and Verifying the Timing Properties of Hard- Real-Time Programming Languages and Systems. Fort Belvoir, VA: Defense Technical Information Center, junho de 1992. http://dx.doi.org/10.21236/ada257296.
Texto completo da fonteBednall, Timothy. A Gentle Introduction to Python. Instats Inc., 2023. http://dx.doi.org/10.61700/ywg7hgz3gf12y469.
Texto completo da fonteBednall, Timothy. A Gentle Introduction to Python. Instats Inc., 2023. http://dx.doi.org/10.61700/oma5ikdj8xru1469.
Texto completo da fonteBednall, Timothy. A Gentle Introduction to R. Instats Inc., 2022. http://dx.doi.org/10.61700/nkdwj37n3trpc469.
Texto completo da fonteBednall, Timothy. A Gentle Introduction to R. Instats Inc., 2022. http://dx.doi.org/10.61700/8851t6mqarw95469.
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