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Journal articles on the topic 'Predictive programming'

Author: Grafiati

Published: 25 May 2024

Last updated: 29 July 2025

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1

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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6

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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Ohmori, Shunichi. "A Predictive Prescription Using Minimum Volume k-Nearest Neighbor Enclosing Ellipsoid and Robust Optimization." Mathematics 9, no. 2 (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 in

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Ohmori, Shunichi. "A Predictive Prescription Using Minimum Volume k-Nearest Neighbor Enclosing Ellipsoid and Robust Optimization." Mathematics 9, no. 2 (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 in

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Xu, Yanhua, Yuqing Zeng, Zhihua Ai, Chang Wang, Guiyu Wang, and Huili Yang. "Predicting Upper Secondary School Students’ Programming Self-efficacy in Tobacco Growing Areas of Southwest China Using Decision Tree Analysis." Tobacco Regulatory Science 7, no. 5 (2021): 4092–100. http://dx.doi.org/10.18001/trs.7.5.1.185.

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Background: In the field of artificial intelligence, programming self-efficacy plays an indispensable role in the success of programming learning. However, how to predict the level of students’ programming self-efficacy has not been addressed. Objective: To predict the level of programming self-efficacy among upper secondary school students in tobacco growing areas of Southwest China, this study used survey data to develop a decision tree model. Methods: First, a total of 512 questionnaires were collected by using the Academic Achievement Test, Creative Style Scale, Programming Learning Attitu

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Lau, Wilfred W. F., and Allan H. K. Yuen. "Predictive Validity of Measures of the Pathfinder Scaling Algorithm on Programming Performance: Alternative Assessment Strategy for Programming Education." Journal of Educational Computing Research 41, no. 2 (2009): 227–50. http://dx.doi.org/10.2190/ec.41.2.e.

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Recent years have seen a shift in focus from assessment of learning to assessment for learning and the emergence of alternative assessment methods. However, the reliability and validity of these methods as assessment tools are still questionable. In this article, we investigated the predictive validity of measures of the Pathfinder Scaling Algorithm (PSA), a concept mapping assessment utility, using the referent-free and referent-based approaches on programming performance of a group of secondary school students. Results suggest that the predictive validity of both approaches was more or less

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Jianwang, Hong, Ricardo A. Ramirez-Mendoza, and Ruben Morales-Menendez. "Introducing Dynamic Programming and Persistently Exciting into Data-Driven Model Predictive Control." Mathematical Problems in Engineering 2021 (May 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/9915994.

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In this paper, one new data-driven model predictive control scheme is proposed to adjust the varying coupling conditions between different parts of the system; it means that each group of linked subsystems is grouped as data-driven scheme, and this group is independently controlled through a decentralized model predictive control scheme. After combing coalitional scheme and model predictive control, coalitional model predictive control is used to design each controller, respectively. As the dynamic programming is only used in optimization theory, to extend its advantage in control theory, the

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Chao-Hsien Hsieh, Chao-Hsien Hsieh, Xinyu Yao Chao-Hsien Hsieh, Ziyi Wang Xinyu Yao, and Hongmei Wang Ziyi Wang. "Efficient Predictive Regulation Algorithms for AGV System in Industrial Internet." 網際網路技術學刊 25, no. 3 (2024): 387–401. http://dx.doi.org/10.53106/160792642024052503005.

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<p>In recent years, the industrial Internet has developed rapidly. In order to improve the reliability, real-time, and economy, Automated Guided Vehicle (AGV) in intelligent manufacturing system becomes an indispensable technology. However, the current AGV system relies too much on the fixed network bandwidth environment in information transmission and management. When the traffic demand changes frequently, this form of network configuration lacks network resource management mechanism. Further, it leads to the problems of delay, waste of network flow, and inability to dynamically allocat

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Neerav Nishant, Nisha Rathore, Vinay Kumar Nassa, Vijay Kumar Dwivedi, Thulasimani T, and Surrya Prakash Dillibabu. "Integrating machine learning and mathematical programming for efficient optimization of electric discharge machining technique." Scientific Temper 14, no. 03 (2023): 859–63. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.46.

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This study focuses on predictive modeling in machining, specifically material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra) prediction using regression analysis. The research employs electrical discharge machining (EDM) experiments to validate the proposed unified predictive model. The approach involves varying machining parameters systematically and collecting empirical data. The dataset is split for training and testing, and advanced regression techniques are used to formulate the model. Evaluation metrics such as R-squared and mean-squared error (MSE) are employed to

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Bubnic, Bostjan, Marjan Mernik, and Tomaž Kosar. "Exploring the Predictive Potential of Complex Problem-Solving in Computing Education: A Case Study in the Introductory Programming Course." Mathematics 12, no. 11 (2024): 1655. http://dx.doi.org/10.3390/math12111655.

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Programming is acknowledged widely as a cornerstone skill in Computer Science education. Despite significant efforts to refine teaching methodologies, a segment of students is still at risk of failing programming courses. It is crucial to identify potentially struggling students at risk of underperforming or academic failure. This study explores the predictive potential of students’ problem-solving skills through dynamic, domain-independent, complex problem-solving assessment. To evaluate the predictive potential of complex problem-solving empirically, a case study with 122 participants was co

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Wu, Sheng, Pingzhi Hou, and Hongbo Zou. "An improved constrained predictive functional control for industrial processes: A chamber pressure process study." Measurement and Control 53, no. 5-6 (2020): 833–40. http://dx.doi.org/10.1177/0020294019881739.

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An improved constrained predictive functional control for the pressure of a coke furnace is proposed in this article. In conventional constrained model predictive control, a quadratic programming problem is usually constructed to replace the original cost function and constraints to obtain the optimal control law. Under strict constraints, however, the relevant quadratic programming problem may have no feasible solutions. Unlike conventional approaches, there are several effective relaxations introduced for the constraints in the proposed scheme; then, a new cost function and the new transform

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Maitland, Anson, and John McPhee. "Accelerated Model Predictive Control Using Restricted Quadratic Programming." IFAC-PapersOnLine 53, no. 2 (2020): 7001–6. http://dx.doi.org/10.1016/j.ifacol.2020.12.439.

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Asalkhanov, Petr, Yaroslav Ivanio, and Marina Polkovskaya. "CROP YIELD PREDICTIVE MODELS IN PARAMETRIC PROGRAMMING PROBLEMS." Proceedings of Irkutsk State Technical University 21, no. 2 (2017): 57–66. http://dx.doi.org/10.21285/1814-3520-2017-2-57-66.

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22

Hong, Haichao, Arnab Maity, Florian Holzapfel, and Shengjing Tang. "Model Predictive Convex Programming for Constrained Vehicle Guidance." IEEE Transactions on Aerospace and Electronic Systems 55, no. 5 (2019): 2487–500. http://dx.doi.org/10.1109/taes.2018.2890375.

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23

Soufian, Mustapha, David J Sandoz, and Majeed Soufian. "Dynamic Programming Approach for Constrained Model Predictive Control." IFAC Proceedings Volumes 30, no. 27 (1997): 219–24. http://dx.doi.org/10.1016/s1474-6670(17)41184-0.

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Grosman, Benyamin, and Daniel R. Lewin. "Automated nonlinear model predictive control using genetic programming." Computers & Chemical Engineering 26, no. 4-5 (2002): 631–40. http://dx.doi.org/10.1016/s0098-1354(01)00780-3.

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25

Saffer, Daniel R., and Francis J. Doyle. "Analysis of linear programming in model predictive control." Computers & Chemical Engineering 28, no. 12 (2004): 2749–63. http://dx.doi.org/10.1016/j.compchemeng.2004.08.007.

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Bai, Yanyang, and Xuesheng Zhang. "Prediction Model of Football World Cup Championship Based on Machine Learning and Mobile Algorithm." Mobile Information Systems 2021 (September 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/1875060.

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With the technological development and change of the times in the current era, with the rapid development of science and technology and information technology, there is a gradual replacement in the traditional way of cognition. Effective data analysis is of great help to all societies, thereby drive the development of better interests. How to expand the development of the overall information resources in the process of utilization, establish a mathematical analysis–oriented evidence theory system model, improve the effective utilization of the machine, and achieve the goal of comprehensively p

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27

Lee, Junho, and Hyuk-Jun Chang. "Multi-parametric model predictive control for autonomous steering using an electric power steering system." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 13 (2019): 3391–402. http://dx.doi.org/10.1177/0954407018824773.

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Electric power steering systems have been used to generate assist torque for driver comfort. This study makes use of the functionality of electric power steering systems for autonomous steering control without driver torque. A column-type electric power steering test bench, equipped with a brushless DC motor as an assist motor, and the Infineon TriCore AURIX TC 277 microcontroller was used in this study. Multi-parametric model predictive control is based on a model predictive control–based approach that employs a multi-parametric quadratic programming technique. This technique allows the reduc

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Wang, Zhaohong, Jia Huang, Caixue Chen, and Seiji Fukushima. "Design of Prediction-Based Controller for Networked Control Systems with Packet Dropouts and Time-Delay." Mathematical Problems in Engineering 2022 (January 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/9437955.

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A novel prediction-based controller design is proposed for networked control systems (NCSs) with stochastic packet dropouts and time-delay in their control channel. The sequence of packet dropouts, which are modelled as a Bernoulli process, is compensated by a zero-order holder (ZOH)-based module, whereas a state predictor is utilized for obtaining the predicted states at the time delayed. In view of dropout compensator and state predictor, a novel modified model predictive controller (MPC) is designed and proposed in the following procedures. Compared to cost function of a general model predi

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Hung, Tran Dang. "Enhanced Predictive Data Modeling for Specialized Sciences using Least Squares Convex Optimization." Scholars Journal of Engineering and Technology 13, no. 05 (2025): 320–27. https://doi.org/10.36347/sjet.2025.v13i05.002.

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This paper introduces an improved approach to predictive data modeling by leveraging least squares optimization and global convex analysis. We begin with the construction of a linear predictive model and apply the least squares method to minimize residual error. Subsequently, we incorporate global convex optimization techniques to refine the model using quadratic forms. This approach offers enhanced prediction accuracy and robustness for specialized scientific datasets. The methodology is further translated into algorithmic pseudocode suitable for large-scale data programming. Real-world examp

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McCarl, Bruce A., and Jeffrey Apland. "Validation of Linear Programming Models." Journal of Agricultural and Applied Economics 18, no. 2 (1986): 155–64. http://dx.doi.org/10.1017/s0081305200006208.

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AbstractSystematic approaches to validation of linear programming models are discussed for prescriptive and predictive applications to economic problems. Specific references are made to a general linear programming formulation, however, the approaches are applicable to mathematical programming applications in general. Detailed procedures are outlined for validating various aspects of model performance given complete or partial sets of observed, real world values of variables. Alternative evaluation criteria are presented along with procedures for correcting validation problems.

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Thube, Komal Bhaskar. "Prophecy on Programming Language using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3699–706. http://dx.doi.org/10.22214/ijraset.2021.35746.

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A programming language is a computer language developers use to develop software programs, scripts, or other sets of instruction for computers to execute. It is difficult to determine which programming language is widely used. In our work, I have analyzed and compared the classification results of various machine learning models and find out which programming language is widely used by developers. I have used Support Vector Machine (SVM), K neighbor classifier (KNN),Decision Tree Classifier(CART) for our comparative study. My task is to analyze different data and to classify them for the effic

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Wang, Binyu, Yulong Lei, Yao Fu, and Xiaohu Geng. "Research on gear decision method of commercial vehicle based on predictive road information." Advances in Mechanical Engineering 14, no. 7 (2022): 168781322211145. http://dx.doi.org/10.1177/16878132221114594.

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As an essential part of the transportation industry, it is necessary to reduce the fuel consumption of commercial vehicles from the perspective of the environment and economy. Previous studies have shown that optimizing the gear sequence can reduce vehicle fuel consumption. This paper presents a gear decision method based on predictive road information. Under the model predictive control framework, the dynamic programming algorithm is used to solve the multi-objective optimization problem of gear decision. To solve the problem of the long calculation time of the dynamic programming algorithm,

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Todini, E. "The role of predictive uncertainty in the operational management of reservoirs." Proceedings of the International Association of Hydrological Sciences 364 (September 16, 2014): 118–22. http://dx.doi.org/10.5194/piahs-364-118-2014.

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Abstract. The present work deals with the operational management of multi-purpose reservoirs, whose optimisation-based rules are derived, in the planning phase, via deterministic (linear and nonlinear programming, dynamic programming, etc.) or via stochastic (generally stochastic dynamic programming) approaches. In operation, the resulting deterministic or stochastic optimised operating rules are then triggered based on inflow predictions. In order to fully benefit from predictions, one must avoid using them as direct inputs to the reservoirs, but rather assess the "predictive knowledge" in te

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Jianhong, Wang, and Ricardo A. Ramirez-Mendoza. "Application of Interval Predictor Model Into Model Predictive Control." WSEAS TRANSACTIONS ON SYSTEMS 20 (January 6, 2022): 331–43. http://dx.doi.org/10.37394/23202.2021.20.38.

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In this paper, interval prediction model is studied for model predictive control (MPC) strategy with unknown but bounded noise. After introducing the family of models and some basic information, some computational results are presented to construct interval predictor model, using linear regression structure whose regression parameters are included in a sphere parameter set. A size measure is used to scale the average amplitude of the predictor interval, then one optimal model that minimizes this size measure is efficiently computed by solving a linear programming problem. The active set approa

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Dalinghaus, Charline, Giovanni Coco, and Pablo Higuera. "A predictive equation for wave setup using genetic programming." Natural Hazards and Earth System Sciences 23, no. 6 (2023): 2157–69. http://dx.doi.org/10.5194/nhess-23-2157-2023.

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Abstract. We applied machine learning to improve the accuracy of present predictors of wave setup. Namely, we used an evolutionary-based genetic programming model and a previously published dataset, which includes various beach and wave conditions. Here, we present two new wave setup predictors: a simple predictor, which is a function of wave height, wavelength, and foreshore beach slope, and a fitter, but more complex predictor, which is also a function of sediment diameter. The results show that the new predictors outperform existing formulas. We conclude that machine learning models are cap

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Bartlett, Roscoe A., Lorenz T. Biegler, Johan Backstrom, and Vipin Gopal. "Quadratic programming algorithms for large-scale model predictive control." Journal of Process Control 12, no. 7 (2002): 775–95. http://dx.doi.org/10.1016/s0959-1524(02)00002-1.

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Lee, Y. I., and B. Kouvaritakis. "A linear programming approach to constrained robust predictive control." IEEE Transactions on Automatic Control 45, no. 9 (2000): 1765–70. http://dx.doi.org/10.1109/9.880645.

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Huppertz, Berthold. "Maternal–fetal interactions, predictive markers for preeclampsia, and programming." Journal of Reproductive Immunology 108 (April 2015): 26–32. http://dx.doi.org/10.1016/j.jri.2014.11.003.

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Brand, Matthew, Vijay Shilpiekandula, Chen Yao, et al. "A Parallel Quadratic Programming Algorithm for Model Predictive Control." IFAC Proceedings Volumes 44, no. 1 (2011): 1031–39. http://dx.doi.org/10.3182/20110828-6-it-1002.03222.

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Boiroux, Dimitri, та John Bagterp Jørgensen. "Sequential ℓ1 Quadratic Programming for Nonlinear Model Predictive Control". IFAC-PapersOnLine 52, № 1 (2019): 474–79. http://dx.doi.org/10.1016/j.ifacol.2019.06.107.

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Pistikopoulos, E. N. "Perspectives in multiparametric programming and explicit model predictive control." AIChE Journal 55, no. 8 (2009): 1918–25. http://dx.doi.org/10.1002/aic.11965.

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Ankala*, Dr Krishna Mohan, and Jyothirmai Kanigolla. "Railway Infrastructure and Traveller usage Prediction and Rendering Solutions." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (2019): 915–17. http://dx.doi.org/10.35940/ijitee.j9296.0981119.

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This project introduces the primary establishments of Big Data connected to Smart Cities. An IOT based mechanism is proposed to be connected to various areas. In this project, we are trying to predict and provide the solution to improvise the railway / bus infrastructure and their services. Indian local & state railways or buses are a mode of transport service where thousands of people process every minute. Thus our proposed system involves data collection of the users based on id, username, gender, age, the timing of travel, station source and destination to monitor the user travel behavi

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Baig, Ulfat, Prajakta Belsare, Milind Watve, and Maithili Jog. "Can Thrifty Gene(s) or Predictive Fetal Programming for Thriftiness Lead to Obesity?" Journal of Obesity 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/861049.

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Obesity and related disorders are thought to have their roots in metabolic “thriftiness” that evolved to combat periodic starvation. The association of low birth weight with obesity in later life caused a shift in the concept from thrifty gene to thrifty phenotype or anticipatory fetal programming. The assumption of thriftiness is implicit in obesity research. We examine here, with the help of a mathematical model, the conditions for evolution of thrifty genes or fetal programming for thriftiness. The model suggests that a thrifty gene cannot exist in a stable polymorphic state in a population

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Ławryńczuk, Maciej, and Piotr Tatjewski. "Nonlinear predictive control based on neural multi-models." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 7–21. http://dx.doi.org/10.2478/v10006-010-0001-y.

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Nonlinear predictive control based on neural multi-modelsThis paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discus

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Zou, Hongbo, and Limin Wang. "An improved constrained dynamic matrix control for temperature in an industrial coke furnace." Measurement and Control 52, no. 5-6 (2019): 409–17. http://dx.doi.org/10.1177/0020294019838589.

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In order to derive the feasible control law of the constrained model predictive control scheme, quadratic programming has been introduced as an effective method. It is known that the typical performance index for model predictive control strategies under various constraints can be converted into a standard quadratic programming problem; however, there may be no feasible solutions for the corresponding quadratic programming problem when the working conditions are too bad or constraints are too rigorous, the real-time control law cannot be updated and the system performance may be deteriorated.

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Zhang, Wen Tao, Rui Feng An, and Bin Wu. "The Application of Global Forecast Function in the Power System Load Forecasting Software Development." Applied Mechanics and Materials 313-314 (March 2013): 1347–52. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1347.

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First of all, this paper analyzes the calculation process of the load forecast of power system, and puts forward a new ideas of varieties of load forecasting method according to the classification on this basis, this paper will establish a global predictive function corresponding to different kinds of load forecasting methods, and designed the software on the load forecasting on the basis of such function. This paper describes the programming ideas of using the global predictive function to conduct the single forecast method and combined forecast, which demonstrates its advantages. Finally thi

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Kusznir, Tom, and Jarosław Smoczek. "Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane." Applied Sciences 14, no. 12 (2024): 5112. http://dx.doi.org/10.3390/app14125112.

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This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) alg

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Hao, Jinna, Shumin Ruan, and Wei Wang. "Model Predictive Control Based Energy Management Strategy of Series Hybrid Electric Vehicles Considering Driving Pattern Recognition." Electronics 12, no. 6 (2023): 1418. http://dx.doi.org/10.3390/electronics12061418.

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This paper proposes an energy management strategy for a series hybrid electric vehicle based on driving pattern recognition, driving condition prediction, and model predictive control to improve the fuel consumption while maintain the state of charge of the battery. To further improve the computational efficiency, the discretization and linearization of the model is conducted, and the MPC problem is transferred into a quadratic programming problem, which can be solved by the interior point method effectively. The simulation is carried out by using Matlab/Simulink platform, and the simulation r

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Bulhakova, Olha, Yuliia Ulianovska, Victoria Kostenko, and Tatyana Rudyanova. "Consideration of the possibilities of applying machine learning methods for data analysis when promoting services to bank's clients." Technology audit and production reserves 4, no. 2(66) (2022): 14–18. http://dx.doi.org/10.15587/2706-5448.2022.262562.

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The object of the research is modern online services and machine learning libraries for predicting the probability of the bank client's consent to the provision of the proposed services. One of the most problematic areas is the high unpredictability of the result in the field of banking marketing using the most common technique of introducing new services for clients – the so-called cold calling. Therefore, the question of assessing the probability and predicting the behavior of a potential client when promoting new banking services and services using cold calling is particularly relevant. In

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Ahmad, Nazaruddin, Saifan Hafizh, and Rana Sulthanah. "Prediksi Kelulusan Mata Kuliah Mahasiswa Teknologi Informasi Menggunakan Algoritma K-Nearest Neighbor." Jurnal Manajemen Informatika (JAMIKA) 14, no. 2 (2024): 135–49. http://dx.doi.org/10.34010/jamika.v14i2.12454.

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This research aims to develop a predictive model using the K-Nearest Neighbor (KNN) method to forecast the course completion of students in the Information Technology program. The issue at hand is the uncertainty in predicting student success based on historical data and specific attributes. This study focuses on the importance of understanding the factors that influence student success in the Database Management Systems course to provide accurate predictions and help improve student pass rates in this course. The objective of this research is to build a predictive model using the KNN algorith

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