Relevant bibliographies by topics / Predictive programming

Contents

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

Academic literature on the topic 'Predictive programming'

Author: Grafiati
Published: 25 May 2024
Last updated: 29 July 2025

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Journal articles on the topic "Predictive programming"

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de Madrid, A. P., S. Dormido, F. Morilla, and L. Grau. "Dynamic Programming Predictive Control." IFAC Proceedings Volumes 29, no. 1 (1996): 1721–26. http://dx.doi.org/10.1016/s1474-6670(17)57917-3.

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Kulcsár, Zsuzsanna, János Nagy, and Mária Nábrády. "Hemisphericity and predictive motor programming." International Journal of Psychophysiology 11, no. 1 (1991): 49. http://dx.doi.org/10.1016/0167-8760(91)90209-g.

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Xie, Haotian, Jianming Du, Dongliang Ke, et al. "Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution." Symmetry 14, no. 3 (2022): 626. http://dx.doi.org/10.3390/sym14030626.

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Due to its merits of fast dynamic response, flexible inclusion of constraints and the ability to handle multiple control targets, model predictive control has been widely applied in the symmetry topologies, e.g., electrical drive systems. Predictive current control is penalized by the high current ripples at steady state because only one switching state is employed in every sampling period. Although the current quality can be improved at a low switching frequency by the extension of the prediction horizon, the number of searched switching states will grow exponentially. To tackle the aforement
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Rodríguez, Arturo, and Joaquín Trigueros. "Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming." Estudios de Administración 15, no. 2 (2020): 47. http://dx.doi.org/10.5354/0719-0816.2008.56413.

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In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the performance of traditional mechanical forecasting (ARIMA) models and models developed using Genetic Programming we observe that Genetic Programming can be used to create time-series models for quarterly earnings as accurate as the traditional linear models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combina
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Rao, Christopher V., and James B. Rawlings. "Linear programming and model predictive control." Journal of Process Control 10, no. 2-3 (2000): 283–89. http://dx.doi.org/10.1016/s0959-1524(99)00034-7.

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Jianhong, Wang. "Dynamic Programming in Data Driven Model Predictive Control?" WSEAS TRANSACTIONS ON SYSTEMS 20 (July 21, 2021): 170–77. http://dx.doi.org/10.37394/23202.2021.20.19.

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In this short note, one data driven model predictive control is studied to design the optimal control sequence. The idea of data driven means the actual output value in cost function for model predictive control is identi_ed through input-output observed data in case of unknown but bounded noise and martingale di_erence sequence. After substituting the identi_ed actual output in cost function, the total cost function in model predictive control is reformulated as the other standard form, so that dynamic programming can be applied directly. As dynamic programming is only used in optimization th
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Babu, Mr M. Jeevan. "Mental Health Prediction Using Catboost Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3449–53. http://dx.doi.org/10.22214/ijraset.2024.59219.

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Abstract: This study investigates the application of the CatBoost algorithm in predicting mental health outcomes using Python programming language. Mental health prediction is a critical area of research due to its significant impact on individuals and society. Traditional predictive modeling techniques often encounter challenges in handling complex and highdimensional data inherent in mental health datasets. CatBoost , a state- of-the-art gradient boosting algorithm, has shown promise in effectively addressing these challenges by handling categorical variables seamlessly and exhibiting robust
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Jiang, Le, Shingo Yamaguchi, and Mohd Anuaruddin Bin Ahmadon. "SynergyAI: A Human–AI Pair Programming Tool Based on Dataflow." Information 16, no. 3 (2025): 178. https://doi.org/10.3390/info16030178.

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This paper proposes SynergyAI, an AI–human pair programming tool that represents predictive models as dataflows composed of AI, input, and output nodes. By visualizing decision tree models and integrating them with dataflow diagrams, SynergyAI effectively addresses the machine learning black-box problem. Additionally, the tool leverages comprehensive prediction algorithms and ensemble learning to simplify the operation of complex dataflows and mitigate overfitting risks. SynergyAI also features an AI assistant that utilizes scatter plot matrices and data correlation analysis to help programmer
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Dixon, Kevin R., John M. Dolan, and Pradeep K. Khosla. "Predictive Robot Programming: Theoretical and Experimental Analysis." International Journal of Robotics Research 23, no. 9 (2004): 955–73. http://dx.doi.org/10.1177/0278364904044401.

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Davidson, Curt, and Alan Ewert. "College Student Commitment and Outdoor Orientation Programming." Journal of Experiential Education 43, no. 3 (2020): 299–316. http://dx.doi.org/10.1177/1053825920923709.

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Background: Increasingly colleges and universities are utilizing Outdoor Orientation Programs (OOPs) to help incoming students assimilate into college life. These programs have shown promise in recent analyses for enhancing desired outcomes with particular consideration shown to pro-social behavior and retention outcomes. Purpose: To examine how effective OOPs are in preparing students for a successful college student experience, particularly with variables known to influence student success and commitment to college. Methodology/Approach: Data were collected from four universities across the
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Dissertations / Theses on the topic "Predictive programming"

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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.

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<p>Avhandling 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.</p>
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Buerger, Johannes Albert. "Fast model predictive control." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:6e296415-f02c-4bc2-b171-3bee80fc081a.

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This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-ba
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Freiwat, Sami, and 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.

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Platooning is an interesting area which involve the possibility of decreasing the fuel consumption of heavy-duty vehicles. By reducing the inter-vehicle spacing in the platoon we can reduce air drag, which in turn reduces fuel consumption. Two fuel-efficient model predictive controllers for HDVs in a platoon has been formulated in this master thesis, both utilizing road grade preview information. The first controller is based on linear programming (LP) algorithms and the second on quadratic programming (QP). These two platooning controllers are compared with each other and with generic control
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Felipe, 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.

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Rivotti, Pedro. "Multi-parametric programming and explicit model predictive control of hybrid systems." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24432.

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This thesis is concerned with different topics in multi-parametric programming and explicit model predictive control, with particular emphasis on hybrid systems. The main goal is to extend the applicability of these concepts to a wider range of problems of practical interest, and to propose algorithmic solutions to challenging problems such as constrained dynamic programming of hybrid linear systems and nonlinear explicit model predictive control. The concepts of multi-parametric programming and explicit model predictive control are presented in detail, and it is shown how the solution to expl
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Bennett, Andrew David. "Using genetic programming to learn predictive models from spatio-temporal data." Thesis, University of Leeds, 2010. http://etheses.whiterose.ac.uk/1376/.

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This thesis describes a novel technique for learning predictive models from nondeterministic spatio-temporal data. The prediction models are represented as a production system, which requires two parts: a set of production rules, and a conflict resolver. The production rules model different, typically independent, aspects of the spatio-temporal data. The conflict resolver is used to decide which sub-set of enabled production rules should be fired to produce a prediction. The conflict resolver in this thesis can probabilistically decide which set of production rules to fire, and allows the syst
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Jonsson, 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.

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<p>The 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
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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.

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The system for mission handling in the Gripen fighter aircraft, and in its ground supporting system, consists for example of ways to plan mission routes, create mission points and validate performed missions. The system is complex and for example, the number of different mission points used increases due to changing demands and needs. This master thesis presents suggestions for improvements and simplifications for the mission handling system, to make it more intuitive and more friendly to use. As a base for the suggestions, interviews with pilots from Saab, TUJAS and FMV have been conducted, t
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Hellström, Erik. "Look-ahead Control of Heavy Trucks utilizing Road Topography." Licentiate thesis, Linköping University, Linköping University, Vehicular Systems, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9262.

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<p>The power to mass ratio of a heavy truck causes even moderate slopes to have a significant influence on the motion. The velocity will inevitable vary within an interval that is primarily determined by the ratio and the road topography. If further variations are actuated by a controller, there is a potential to lower the fuel consumption by taking the upcoming topography into account. This possibility is explored through theoretical and simulation studies as well as experiments in this work.</p><p>Look-ahead control is a predictive strategy that repeatedly solves an optimization problem onli
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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.

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Books on the topic "Predictive programming"

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author, Mayor Eric, and Forte Rui Miguel author, eds. R: Predictive analysis : master the art of predictive modeling. Packt Publishing, 2017.

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C, Georgiadis Michael, Pistikopoulos Efstratios N, and Dua Vivek, eds. Multi-parametric model-based control: Theory and applications. Wiley-VCH, 2007.

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Fahringer, Thomas. Automatic performance prediction of parallel programs. Kluwer Academic Publishers, 1996.

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Hyslop, William F. Performance prediction of relational database management systems. Computer Systems Research Institute, University of Toronto, 1991.

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Fahringer, Thomas. Automatic Performance Prediction of Parallel Programs. Springer US, 1996.

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R, Horn J., and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Geometric programming prediction of design trends for OMV protective structures. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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Rauscher, Harold M. The microcomputer scientific software series 4: Testing prediction accuracy. U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station, 1986.

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Brown, Robert Goodell. Smoothing, forecasting and prediction of discrete time series. Dover Publications, 2004.

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United States. National Aeronautics and Space Administration., ed. TBIEM3D: A computer program for predicting ducted fan engine noise. National Aeronautics and Space Administration, Langley Research Center, 1997.

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Paul, Bishop, and Educational Systems Associates, eds. Adapted physical education: A comprehensive resource manual of definition, assessment, programming and future prediction. Educational Systems Associates, Inc., 1988.

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Book chapters on the topic "Predictive programming"

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Grancharova, Alexandra, and Tor Arne Johansen. "Multi-parametric Programming." In Explicit Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_1.

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Mathavaraj, S., and Radhakant Padhi. "Model Predictive Static Programming." In Satellite Formation Flying. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9631-5_7.

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Ferreira, Carlos Abreu, João Gama, and Vítor Santos Costa. "Predictive Sequence Miner in ILP Learning." In Inductive Logic Programming. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31951-8_15.

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Saerens, Bart, Moritz Diehl, and Eric Van den Bulck. "Optimal Control Using Pontryagin’s Maximum Principle and Dynamic Programming." In Automotive Model Predictive Control. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_8.

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Marathe, Madhav V. "Towards a Predictive Computational Complexity Theory." In Automata, Languages and Programming. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45465-9_2.

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Kirches, Christian. "Constrained Nonlinear Programming." In Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8_4.

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Zavala, Victor M., and Lorenz T. Biegler. "Nonlinear Programming Strategies for State Estimation and Model Predictive Control." In Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_33.

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Šourek, Gustav, Suresh Manandhar, Filip Železný, Steven Schockaert, and Ondřej Kuželka. "Learning Predictive Categories Using Lifted Relational Neural Networks." In Inductive Logic Programming. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63342-8_9.

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Grüne, Lars. "Dynamic Programming, Optimal Control and Model Predictive Control." In Handbook of Model Predictive Control. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77489-3_2.

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Tamaddoni-Nezhad, Alireza, David Bohan, Alan Raybould, and Stephen Muggleton. "Towards Machine Learning of Predictive Models from Ecological Data." In Inductive Logic Programming. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23708-4_11.

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Conference papers on the topic "Predictive programming"

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Hao, Changlong, Yuan Zhang, and Yuanqing Xia. "Sequential Convex Programming based Model Predictive Control for Unprotected Left-Turn." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662263.

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Kartiwi, Mira, Teddy Surya Gunawan, and Nelidya Md Yusoff. "Predictive Analytics for Learning Performance in First-Year University Programming Course." In 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). IEEE, 2024. http://dx.doi.org/10.1109/icsima62563.2024.10675540.

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Borja-Conde, Jose A., Juan M. Nadales, Filiberto Fele, and Daniel Limon. "Economic model predictive control for periodic operation: a quadratic programming approach." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886231.

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Ngo, Tri, and Cornel Sultan. "Towards Automation of Helicopter Landings on Ship Decks Using Integer Programming and Model Predictive Control." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0074-2018-12783.

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A novel method for the automated control of helicopters in landing maneuvers on ship decks is proposed, which combines integer programming and model predictive control (MPC). The helicopter is first brought sufficiently close to the landing deck using a standard MPC. In the final phase of the mission, termed the rendezvous phase, implementation of the novel control design method allows the MPC to rapidly adapt to the landing deck state via a variable prediction horizon. The control design problem includes an integer variable vector which is used to frequently estimate an appropriate prediction
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Bishop, Arun L., John Z. Zhang, Swaminathan Gurumurthy, Kevin Tracy, and Zachary Manchester. "ReLU-QP: A GPU-Accelerated Quadratic Programming Solver for Model-Predictive Control." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611249.

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Nayak, Sidhidatri, Reshu Agarwal, Sunil Kumar Khatri, and Masoud Mohamaddian. "Optimizing Predictive Model for Student Programming Ability using Regularized Multiple Regression Analysis." In 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2024. https://doi.org/10.1109/pdgc64653.2024.10983954.

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Goller, Tim, Andreas Völz, and Knut Graichen. "A Programming by Demonstration Approach for Robotic Manipulation with Model Predictive Interaction Control." In 2024 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2024. http://dx.doi.org/10.1109/ccta60707.2024.10666568.

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Akundi, Sahithi Srijana, Yuanxing Liu, Austin Braniff, et al. "Integrating Dynamic Risk Assessment with Explicit Model Predictive Control via Chance-Constrained Programming." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.176554.

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Maintaining operational efficiency while ensuring safety is a longstanding challenge in industrial process control, particularly in high-risk environments. This paper presents a novel Dynamic Risk-Informed Explicit Model Predictive Control (R-eMPC) framework that integrates safety and operational objectives using probabilistic constraints and real-time risk assessments. Unlike traditional approaches, this framework dynamically adjusts safety thresholds based on Bayesian updates, ensuring a balanced trade-off between reliability and efficiency. The validation of this approach is illustrated thr
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Kim, Da-Hwi, Jaell Jang, and Chang-Hun Lee. "Rocket Landing Guidance with Linearized Model Predictive Control via Second-order Cone Programming." In 2025 IEEE Aerospace Conference. IEEE, 2025. https://doi.org/10.1109/aero63441.2025.11068520.

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Dantas, Danilo Medeiros, Jucelio Soares dos Santos, Kézia de Vasconcelos Oliveira Dantas, Wilkerson L. Andrade, João Brunet, and 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.

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This study aimed to evaluate the reliability of an item bank developed in the Screening Programming system for measuring predictive programming skills. The results revealed that the selected items showed good content analysis and consistent psychometric properties. Furthermore, the instruments created from this item bank demonstrated good reliability in professional assessments, validating their accuracy and stability across different contexts and populations. These findings contribute to the programming field by providing a reliable instrument for assessing and developing predictive skills in
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Reports on the topic "Predictive programming"

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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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Brown, Dr Brendan, and Dr Rick Llewellyn. Improving Agricultural Policy and Programming through Data-Driven Adoption Prediction. Asian Productivity Organization, 2024. http://dx.doi.org/10.61145/cwao6767.

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This report evaluates the Adoption and Diffusion Outcome Prediction Tool (ADOPT) as a decision support system for agricultural policy across APO member economies. Through validation studies in Bangladesh, India, and Lao PDR, the research demonstrates ADOPT's strength in predicting peak adoption rates while identifying its tendency to overestimate adoption speed in developing contexts. The study introduces the 'Four Ps' framework and concludes that ADOPT, combined with structured analysis methods, provides valuable support for data-driven agricultural policy decisions in Asian contexts.
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Fogel, Lawrence J., and David Fogel. Artificial Intelligence through Evolutionary Programming: Prediction and Identification. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada171544.

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Neely, Christopher J., and 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.

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Shaw, Alan C. Specifying, Predicting, and Verifying the Timing Properties of Hard- Real-Time Programming Languages and Systems. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada257296.

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Bednall, Timothy. A Gentle Introduction to Python. Instats Inc., 2023. http://dx.doi.org/10.61700/ywg7hgz3gf12y469.

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This seminar teaches the basics of Python without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work, manipulate data, and create figures/plots. We will also showcase basic examples of using Python for prediction with regression analysis, classification, dimensionality reduction, and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to Python. Instats Inc., 2023. http://dx.doi.org/10.61700/oma5ikdj8xru1469.

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This seminar teaches the basics of Python without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work, manipulate data, and create figures/plots. We will also showcase basic examples of using Python for prediction with regression analysis, classification, dimensionality reduction, and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to R. Instats Inc., 2022. http://dx.doi.org/10.61700/nkdwj37n3trpc469.

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This seminar teaches the basics of R without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work using R Markdown, manipulate data using the tidyverse, and create great figures using ggplot2. We will also showcase basic examples of using R for prediction, classification, dimensionality reduction and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Bednall, Timothy. A Gentle Introduction to R. Instats Inc., 2022. http://dx.doi.org/10.61700/8851t6mqarw95469.

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This seminar teaches the basics of R without any assumed prior knowledge of statistics or programming. Over the course of two days you'll learn how to load, save, and explore data, present your work using R Markdown, manipulate data using the tidyverse, and create great figures using ggplot2. We will also showcase basic examples of using R for prediction, classification, dimensionality reduction and clustering. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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