Journal articles: 'Predictive programming'

1

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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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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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 (September 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 the same. Among the three similarity measures applied for the referent-based approach, PRX appeared to be the most predictive one whereas PFC and GTD were similar in terms of predictive power. The correlations between the referent-free measure C and the three previously mentioned referent-based measures with the programming performance measures were not as strong as reported in the literature. In the light of these results, we argue that there is a need to reform assessment in programming education.

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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 idea of dynamic programming is applied to analyze the minimum principle and stability for the data-driven model predictive control. Further, the goal of this short note is to bridge the dynamic programming with model predictive control. Through adding the inequality constraint to the constructed model predictive control, one persistently exciting data-driven model predictive control is obtained. The inequality constraint corresponds to the condition of persistent excitation, coming from the theory of system identification. According to the numerical optimization theory, the necessary optimality condition is applied to acquire the optimal control input. Finally, one simulation example is used to prove the efficiency of our proposed theory.

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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 (September 30, 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 Attitude Questionnaire, Motivation Scale, Higher-order Thinking Preferences Scale, and Programming Self-efficacy Scale. Secondly, a decision tree model was constructed by SPSS modeler 18.0. Results: The results showed that academic achievement, creativity style, programming learning, motivation, and higher-order thinking propensity were highly predictive of programming self-efficacy. Conclusions: This is the first study in the direction of educational technology and it represents a novel approach to predicting programming self-efficacy among upper secondary school students. The experimental analysis demonstrate that the encouraging results prove the practical feasibility of the approach.

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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 (September 30, 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 assess the model’s accuracy. Notable findings include accurate predictions for MRR, TWR, and Ra. This approach demonstrates the potential for real-world application, aiding decision-making processes and enhancing machining efficiency. The research underscores the importance of predictive modeling in manufacturing optimization, offering insights into refining model architectures, data preprocessing techniques, and feature selection. The findings affirm the relevance and applicability of predictive modeling in manufacturing, emphasizing its potential to elevate precision and efficiency

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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 (February 2017): 57–66. http://dx.doi.org/10.21285/1814-3520-2017-2-57-66.

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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 (October 2019): 2487–500. http://dx.doi.org/10.1109/taes.2018.2890375.

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Soufian, Mustapha, David J Sandoz, and Majeed Soufian. "Dynamic Programming Approach for Constrained Model Predictive Control." IFAC Proceedings Volumes 30, no. 27 (October 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 (May 2002): 631–40. http://dx.doi.org/10.1016/s0098-1354(01)00780-3.

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

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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 (May 24, 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 conducted in the undergraduate Introductory Programming Course at the University of Maribor, Slovenia. A latent variable approach was employed to examine the associations. The study results showed that complex problem-solving has a strong positive effect on performance in Introductory Programming Courses. According to the results of structural equation modeling, 64% of the variance in programming performance is explained by complex problem-solving ability. Our findings indicate that complex problem-solving performance could serve as a significant, cognitive, dynamic predictor, applicable to the Introductory Programming Course. Moreover, we present evidence that the demonstrated approach could also be used to predict success in the broader computing education community, including K-12, and the wider education landscape. Apart from predictive potential, our results suggest that valid and reliable instruments for assessing complex problem-solving could also be used for assessing general-purpose, domain-independent problem-solving skills in computing education. Likewise, the results confirmed the positive effect of previous programming experience on programming performance. On the other hand, there was no significant direct effect of performance in High School mathematics on Introductory Programming.

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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 (February 17, 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 transformed constraints are generated. With the improved constraints and cost function, there are always acceptable solutions for the quadratic programming problem under various conditions. The validity of the presented constrained model predictive control algorithm is evaluated through the regulation of the pressure of the coke furnace.

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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 predicting the target behavior? The main goal of this article is to use machine learning technology; this article defines the main prediction model by python programming language, analyzes and forecasts the data of previous World Cup, and establishes the analysis and prediction model of football field by K-mean and DPC clustering algorithm. Python programming is used to implement the algorithm. The data of the previous World Cup football matches are selected, and the built model is used for the predictive analysis on the Python platform; the calculation method based on the DPC-K-means algorithm is used to determine the accuracy and probability of the variables through the calculation results, which develops results in specific competitions. Research shows how the machine wins and learns the efficiency of the production process, and the machine learning process, the reliability, and accuracy of the prediction results are improved by more than 55%, which proves that mobile algorithm technology has a high level of predictive analysis on the World Cup football stadium.

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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 (January 22, 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 reduction of the huge computational burden resulting from the online optimization in model predictive control. The proposed controller obtains an optimal input based on multi-parametric quadratic programming at each sampling time. The weighting matrix definition, which is the main task when designing the proposed controller, was analyzed. The experimental results of the step response of the steering wheel angle verified the tracking ability of the proposed controller for different ranges of the prediction horizon. Since the computational loads are directly related to functional safety, the results of this study support the use of the multi-parametric model predictive control scheme as an effective control method for autonomous steering control.

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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 predictive controller, variables of states are substituted by the predicted ones as obtained from state predictor preliminarily. Then, a logical programming approach is applied to include all the possible circumstances in the prediction horizon. Consequently, the cost function is reformed as simultaneous minimax linear matrix inequalities (LMI) with constraints. As a result, toolbox YALMIP is employed in order to solve such minimax programming problem eventually. Simulation results are presented to show the feasibility and performance of proposed method.

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McCarl, Bruce A., and Jeffrey Apland. "Validation of Linear Programming Models." Journal of Agricultural and Applied Economics 18, no. 2 (December 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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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 (July 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, the nonlinear optimization algorithm is used to optimize the dynamic programming sub-problem, and the optimal gear sequence of fuel consumption is obtained. The final optimized gear sequence is obtained through the dynamic programming algorithm. The simulation analysis of the proposed gear shift decision method shows that the gear shift decision method can effectively reduce fuel consumption under fixed working conditions. Compared with the economic shift schedule, the fuel consumption is reduced by 5%, and the computing speed is improved compared with the dynamic programming algorithm.

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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 approach is applied to solve the linear programming problem, and based on these optimization variables, the predictor interval of the considered model with sphere parameter set can be directly constructed. As for choosing a fixed non-negative number in our given size measure, a better choice is proposed by using the Karush-Kuhn-Tucker (KKT) optimality conditions. In order to apply interval prediction model into model predictive control, the midpoint of that interval is substituted in a quadratic optimization problem with inequality constrained condition to obtain the optimal control input. After formulating it as a standard quadratic optimization and deriving its dual form, the Gauss-Seidel algorithm is applied to solve the dual problem and convergence of Gauss-Seidel algorithm is provided too. Finally simulation examples confirm our theoretical results.

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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 (October 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, Scott A. Bortoff, Takehiro Nishiyama, Shoji Yoshikawa, and Takashi Iwasaki. "A Parallel Quadratic Programming Algorithm for Model Predictive Control." IFAC Proceedings Volumes 44, no. 1 (January 2011): 1031–39. http://dx.doi.org/10.3182/20110828-6-it-1002.03222.

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Boiroux, Dimitri, and John Bagterp Jørgensen. "Sequential ℓ1 Quadratic Programming for Nonlinear Model Predictive Control." IFAC-PapersOnLine 52, no. 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 (August 2009): 1918–25. http://dx.doi.org/10.1002/aic.11965.

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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 (June 16, 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 capable of improving predictive capability (when compared to existing predictors) and also of providing a physically sound description of wave setup.

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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 (June 30, 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 efficiency of each algorithm in terms of accuracy, precision, recall, and F1 Score. My best accuracy was 94.29% percent which was found using SVM. These techniques are coded in python and executed in Jupyter NoteBook, the Scientific Python Development Environment. Our experiments have shown that SVM is the best for predictive analysis and from our study that SVM is the well-suited algorithm for the prediction of the most widely used programming language.

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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 terms of a predictive probability density to be operationally used in the decision making process for the estimation of expected benefits and/or expected losses. Using a theoretical and extremely simplified case, it will be shown why directly using model forecasts instead of the full predictive density leads to less robust reservoir management decisions. Moreover, the effectiveness and the tangible benefits for using the entire predictive probability density instead of the model predicted values will be demonstrated on the basis of the Lake Como management system, operational since 1997, as well as on the basis of a case study on the lake of Aswan.

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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. The conditions for evolution of thrifty fetal programming are restricted if the correlation between intrauterine and lifetime conditions is poor. Such a correlation is not observed in natural courses of famine. If there is fetal programming for thriftiness, it could have evolved in anticipation of social factors affecting nutrition that can result in a positive correlation.

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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 (March 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 discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.

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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 (October 30, 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 behavior. Thus the collected data can be used for analytics and prediction. Predicting the consumer's count and behavior who uses the railway services are solved through the R Programming. The data analytics are performed using R studio. For this work, In R programming, we use K-means algorithm for clustering and use Naive Bayes algorithm for machine learning and solution defining. Finally, the predictive output is sent for public access using shinyapps.io. These results are useful to the travelling systems for giving better services to passengers.

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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) (August 11, 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 the course of the study, libraries of machine learning methods and data analysis of the Python programming language were used. A program was developed to build a model for predicting the behavior of bank customers using data processing methods using gradient boosting, regularization of gradient boosting, random forest algorithm and recurrent neural networks. Analogous models were built using cloud machine learning services Azure ML, BigML and the Auto-sklearn library. Data analysis and prediction models built using Python language libraries have a fairly high quality – an average of 94.5 %. Using the Azure ML cloud service, a predictive model with an accuracy of 88.6 % was built. The BigML machine learning service made it possible to build a model with an accuracy of 88.8 %. Machine learning methods from the Auto-sklearn library made it possible to obtain a model with a higher quality – 94.9 %. This is due to the fact that the proposed libraries of the Python programming language allow better customization of data processing methods and machine learning to obtain more accurate models than free cloud services that do not provide such capabilities. Thanks to this, it is possible to obtain a predictive model of the behavior of bank customers with a fairly high degree of accuracy. It is worth noting that in order to make a prediction (forecast), it is necessary to study the context of the task, process the data, build various machine learning algorithms, evaluate the quality of the models and choose the best of them.

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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 this paper has introduced the realization of global predictive function in Visual Basic language in programming, and the corresponding interface-building and use method. Actual use shows that, the use of global predictive function can significantly reduce the program size, add flexibility in the load forecasting software, which improves the scalability as well as the extensibility of the program.

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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 (March 16, 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 results verify the feasibility of the condition prediction method and the performance of the proposed method. In addition, the predictive control strategy successfully improves the fuel economy of the hybrid vehicle compared with the rule-based method.

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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 (April 15, 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. To cope with such problems, an improved quadratic programming problem in which relaxations are employed to increase the possibility of successful solutions is proposed for the constrained dynamic matrix control approach in this paper. By adopting the introduced relaxations, more degrees of relaxations are provided for the optimization process under the case of over-constrained, such that the control law is easier to yield. Case study on the temperature regulation of the coke furnace demonstrates the validity of the improved quadratic programming structure–based dynamic matrix control strategy. Simulation results show that the proposed scheme yields improved control performance.

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Bozorg-Haddad, Omid, Mohammad Delpasand, and Hugo A. Loáiciga. "Self-optimizer data-mining method for aquifer level prediction." Water Supply 20, no. 2 (December 31, 2019): 724–36. http://dx.doi.org/10.2166/ws.2019.204.

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Abstract Groundwater management requires accurate methods for simulating and predicting groundwater processes. Data-based methods can be applied to serve this purpose. Support vector regression (SVR) is a novel and powerful data-based method for predicting time series. This study proposes the genetic algorithm (GA)–SVR hybrid algorithm that combines the GA for parameter calibration and the SVR method for the simulation and prediction of groundwater levels. The GA–SVR algorithm is applied to three observation wells in the Karaj plain aquifer, a strategic water source for municipal water supply in Iran. The GA–SVR's groundwater-level predictions were compared to those from genetic programming (GP). Results show that the randomized approach of GA–SVR prediction yields R2 values ranging between 0.88 and 0.995, and root mean square error (RMSE) values ranging between 0.13 and 0.258 m, which indicates better groundwater-level predictive skill of GA-SVR compared to GP, whose R2 and RMSE values range between 0.48–0.91 and 0.15–0.44 m, respectively.

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Xu, Enyong, Fumin Wei, Changbo Lin, Yanmei Meng, Jihong Zhu, and Xin Liu. "Model predictive control-based energy management strategy with vehicle speed prediction for hybrid electric vehicles." AIP Advances 12, no. 7 (July 1, 2022): 075019. http://dx.doi.org/10.1063/5.0098223.

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The speed of a hybrid electric vehicle is a critical factor that affects its energy management performance. In this study, we focus on the importance of solving the problem of inaccurate speed prediction in the energy management strategy (EMS) and application of dynamic programming (DP) needs to know the entire driving cycle. A gated recurrent unit neural network (GRU-NN) speed predictive model based on machine learning is developed by using the model predictive control (MPC) framework and solved in the prediction domain by employing DP. The neural network is trained on the training set, which is a collection of standard driving cycles. The results are compared with other two types of speed predictive models to verify the effects of different parameters of different speed predictive models on the state of charge and fuel consumption under Urban Dynamometer Driving Schedule driving cycle. Simulation shows that MPC based on the GRU-NN speed predictive model can effectively improve the fuel economy of hybrid electric vehicles, with a 94.14% fuel economy, which proves its application potential. Finally, the GRU-NN speed predictive model is applied under the Real-World Driving Cycle, whose fuel consumption has a fuel economy of 91.95% compared with that of the original rule-based EMS.

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Chen, Zhen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, et al. "iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization." Nucleic Acids Research 49, no. 10 (February 28, 2021): e60-e60. http://dx.doi.org/10.1093/nar/gkab122.

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Abstract Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.

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Leong, H. Y., D. E. L. Ong, J. G. Sanjayan, and A. Nazari. "A genetic programming predictive model for parametric study of factors affecting strength of geopolymers." RSC Advances 5, no. 104 (2015): 85630–39. http://dx.doi.org/10.1039/c5ra16286f.

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Bemporad, A., F. Borrelli, and M. Morari. "Model predictive control based on linear programming - the explicit solution." IEEE Transactions on Automatic Control 47, no. 12 (December 2002): 1974–85. http://dx.doi.org/10.1109/tac.2002.805688.

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Journal articles on the topic 'Predictive programming'

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