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

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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 (June 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 (July 1991): 49. http://dx.doi.org/10.1016/0167-8760(91)90209-g.

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Xie, Haotian, Jianming Du, Dongliang Ke, Yingjie He, Fengxiang Wang, Christoph Hackl, José Rodríguez, and Ralph Kennel. "Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution." Symmetry 14, no. 3 (March 21, 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 aforementioned issue, a fast quadratic programming solver is proposed for multistep predictive current control in this article. First, the predictive current control is described as a quadratic programming problem, in which the objective function is rearranged based on the current derivatives. To avoid the exhaustive search, two vectors close to the reference derivative are preselected in every prediction horizon. Therefore, the number of searched switching states is significantly reduced. Experimental results validate that the predictive current control with a prediction horizon of 5 can achieve an excellent control performance at both steady state and transient state while the computational time is low.
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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 (April 2000): 283–89. http://dx.doi.org/10.1016/s0959-1524(99)00034-7.

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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 (February 4, 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 combinations are sometimes unable to improve on Value Line. Moreover, simple averaging of forecasts results in better predictive accuracy than Genetic Programming-combining of forecasts. Hence, as implemented in this study, Genetic Programming is not superior to traditional methodologies in either forecasting or forecast combining of quarterly earnings.
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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 (March 31, 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 performance in various domains. Leveraging its powerful capabilities, this study aims to develop predictive models for mental health outcomes utilizing a comprehensive dataset encompassing diverse socio- demographic, behavioural , and clinical factors. The predictive performance of the CatBoost algorithm will be evaluated and compared against other commonly used machine learning algorithms, demonstrating its effectiveness in accurately predicting mental health outcomes. This research contributes to the advancement of predictive modeling in mental health research and holds potential implications for personalized interventions and resource allocation in mental healthcare systems
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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 theory, so to extend its advantage in control theory, dynamic programming algorithm is proposed to construct the optimal control sequence. Furthermore, stability analysis for data drive model predictive control is also given based on dynamic programming strategy. Generally, the goal of this short note is to bridge the dynamic programming, system identi_cation and model predictive control. Finally, one simulation example is used to prove the e_ciency of our proposed theory
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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 (September 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 (June 1, 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 United States. Participants in this study were 205 undergraduate students from 17 to 25 years old who self-enrolled in their respective institutions OOP. This study used the College Student Readiness Inventory to generate a hypothesis concerning the possible effects of an OOP experience concerning predictive and outcomes variables relative to college student commitment. Findings/Conclusions: Using SmartPLS, the main effects of the OOP indicated predictive relationships between Commitment to College and Goal Striving, Communication Skills, Social Activity, Emotional Reactivity, Study Skills, and Social Connection. Academic Self-Discipline, Academic Self-Confidence, and Self-Determination on Commitment to College. Implications: Study findings suggest specific connections between predicting college student commitment before and after an OOP.
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Ohmori, Shunichi. "A Predictive Prescription Using Minimum Volume k-Nearest Neighbor Enclosing Ellipsoid and Robust Optimization." Mathematics 9, no. 2 (January 7, 2021): 119. http://dx.doi.org/10.3390/math9020119.

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This paper studies the integration of predictive and prescriptive analytics framework for deriving decision from data. Traditionally, in predictive analytics, the purpose is to derive prediction of unknown parameters from data using statistics and machine learning, and in prescriptive analytics, the purpose is to derive a decision from known parameters using optimization technology. These have been studied independently, but the effect of the prediction error in predictive analytics on the decision-making in prescriptive analytics has not been clarified. We propose a modeling framework that integrates machine learning and robust optimization. The proposed algorithm utilizes the k-nearest neighbor model to predict the distribution of uncertain parameters based on the observed auxiliary data. The enclosing minimum volume ellipsoid that contains k-nearest neighbors of is used to form the uncertainty set for the robust optimization formulation. We illustrate the data-driven decision-making framework and our novel robustness notion on a two-stage linear stochastic programming under uncertain parameters. The problem can be reduced to a convex programming, and thus can be solved to optimality very efficiently by the off-the-shelf solvers.
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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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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.

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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-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.
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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 controllers from Scania. The LP controller proved to be more fuel-efficient than the QP controller, the Scania controllers are however more fuel-efficient than the LP controller.
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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 explicit model predictive control may be efficiently computed using a combination of multi-parametric programming and dynamic programming. A novel algorithm for constrained dynamic programming of mixed-integer linear problems is proposed and illustrated with a numerical example that arises in the context of inventory scheduling. Based on the developments on constrained dynamic programming of mixed-integer linear problems, an algorithm for explicit model predictive control of hybrid systems with linear cost function is presented. This method is further extended to the design of robust explicit controllers for hybrid linear systems for the case when uncertainty is present in the model. The final part of the thesis is concerned with developments in nonlinear explicit model predictive control. By using suitable model reduction techniques, the model captures the essential nonlinear dynamics of the system, while the achieved reduction in dimensionality allows the use of nonlinear multi-parametric programming methods.
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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 system to predict in non-deterministic situations. The predictive models are learnt by a novel technique called Spatio-Temporal Genetic Programming (STGP). STGP has been compared against the following methods: an Inductive Logic Programming system (Progol), Stochastic Logic Programs, Neural Networks, Bayesian Networks and C4.5, on learning the rules of card games, and predicting a person’s course through a network of CCTV cameras. This thesis also describes the incorporation of qualitative temporal relations within these methods. Allen’s intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time are used. The methods are evaluated on the card game Uno, and predicting a person’s course through a network of CCTV cameras. This work is then extended to allow the methods to use qualitative spatial relations. The methods are evaluated on predicting a person’s course through a network of CCTV cameras, aircraft turnarounds, and the game of Tic Tac Toe. Finally, an adaptive bloat control method is shown. This looks at adapting the amount of bloat control used during a run of STGP, based on the ratio of the fitness of the current best predictive model to the initial fitness of the best predictive model.
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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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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 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 %.

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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, this is to obtain opinions and ideas from those using the system and have deep knowledge about it. Another possible assistance and improvement is to provide the possibility of on-line automatic re-planning of the mission route in case of obstacles. MPC (Model Predictive Control) has been used to estimate the obstacle’s flight path,and calculate a new route to the next mission point which does not conflict with the estimated enemy’s path. This system has been implemented in Matlab and the concept is demonstrated with different test scenarios where the design parameters (prediction horizon and penalty in the cost function) for the controller are varied, and stationary and moving obstacles are induced.
Systemet 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.
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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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Jonsson, 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.

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This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.
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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. Birmingham, UK: Packt Publishing, 2017.

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Nitin, Indurkhya, and Zhang Tong 1971-, eds. Fundamentals of predictive text mining. London: Springer-Verlag, 2010.

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Learning data mining with Python: Harness the power of Python to analyze data and create insightful predictive models. Birmingham, UK: Packt Publishing, 2015.

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

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

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

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Fahringer, Thomas. Automatic Performance Prediction of Parallel Programs. Boston, MA: 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. [Washington, D.C.]: 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. St. Paul, Minn: 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. Mineola, NY: Dover Publications, 2004.

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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, 1–37. Berlin, Heidelberg: 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, 111–38. Singapore: 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, 130–44. Berlin, Heidelberg: 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, 119–38. London: 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, 22–31. Berlin, Heidelberg: 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, 61–87. Wiesbaden: 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, 419–32. Berlin, Heidelberg: 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, 108–19. Cham: 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, 29–52. Cham: 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, 154–67. Cham: 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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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 this domain, fostering continuous advancements in understanding and teaching these skills.
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Meadows, 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.

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Beeri, Catriel, and 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.

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Newsom, David K., Sardar F. Azari, Ahmad Anbar, and 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.

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Morgenstern, Dimitri, Daniel Gorges, and 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.

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Chisci, 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.

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Eggimann, Marc-Andre, Oscar D. Crisalle, and 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.

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Cui, Hairong, Wei Wang, and 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.

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Amezquita-Brooks, Luis, and 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.

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Calafiore, G. C., and 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.

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Reports on the topic "Predictive programming"

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Fogel, Lawrence J., and David Fogel. Artificial Intelligence through Evolutionary Programming: Prediction and Identification. Fort Belvoir, VA: Defense Technical Information Center, August 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. Fort Belvoir, VA: Defense Technical Information Center, June 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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Abstract:
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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Abstract:
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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