Relevant bibliographies by topics / Predictive mathematical model

Academic literature on the topic 'Predictive mathematical model'

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Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Predictive mathematical model"

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Chien, Wen T., and W. C. Hung. "Investigation on the Predictive Model for Burr in Laser Cutting Titanium Alloy." Materials Science Forum 526 (October 2006): 133–38. http://dx.doi.org/10.4028/www.scientific.net/msf.526.133.

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The purpose of this study is to develop two predictive models for burr height in cutting titanium alloy plates by using Nd:YAG laser. Firstly, Taguchi method has been used to arrange the experimental scheme and analyze the results via analysis of mean . The important laser cutting parameters affecting burr height can be found. It shows that the pressure of assistant gas, the focusing position and the pulsed frequency are the most important cutting parameters in order. Then they have been chosen as the input variables for response surface methodology and used to construct a mathematical equation for predicting burr height. Secondly, the laser cutting parameters and experimental results obtained from conducting the schematic arrangement using Taguchi method and response surface methodology have been treated as training patterns and recalling patterns for the back-propagation neural network. As a result, a predictive model for burr height prediction in laser cutting titanium alloy has been established. To verify the accuracy of above two prediction models, there are 9 sets of experiment have been performed. It shows that the average error for predicting burr height by the mathematical equation derived from response surface methodology is 5.52% and by the predictive model established by back-propagation neural network is 4.51%, respectively. Obviously, both predictive models are good enough for the relational research and practical applications. It can be concluded that the procedure used in this research and the obtaining predictive models can be used practically in correlate industry.
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Zhang, Cheng Long, Ping Fa Feng, Zhi Jun Wu, and Ding Wen Yu. "A Mathematical Model for Predicting Cutting Force in Rotary Ultrasonic Drilling." Advanced Materials Research 433-440 (January 2012): 2034–41. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2034.

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Rotary ultrasonic machining is a hybrid machining process that combines diamond grinding and ultrasonic machining. The mathematical predictive material removal rate models have been developed in rotary ultrasonic machining with a constant pressure. However, there is no report on mathematical predictive cutting force model in rotary ultrasonic drilling at a constant feedrate presently. Since cutting force can not only reflect the processing state, but also affect the machined surface quality, it is necessary to develop a mathematical model for predicting cutting force which can forecast the machining results. This paper presents a mathematical model to predict the cutting force in rotary ultrasonic machining. On the basis of this model, the relations between cutting force and controllable machining parameters are researched by numerical computation method. This paper also researches the influences of spindle speed and feedrate on cutting force by experiments. The results observed through the experiments agree well with the relations generated from the mathematical model, which verify the developed model.
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Leal-Enríquez, E., and A. R. Gutiérrez-Antúnez. "Indicators of Violence Levels: Questionnaires and Predictive Mathematical Model." Modelling and Simulation in Engineering 2020 (March 17, 2020): 1–9. http://dx.doi.org/10.1155/2020/5857263.

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In this paper, we present, in detail, how a mathematical model that simulates the probable scenarios of intimate partner violence is linked to the application of any questionnaire of domestic violence already in use. This questionnaire assigns a weight of severity to each proposed inquiry for the types of psychological, physical, and sexual violence. We show a numerical procedure that must be performed to obtain the probable scenarios of violence in which the victim is involved, taking as key factor the loss of control of the perpetrator. With the numerical data obtained from the application of the mathematical model, the probable levels of violence that the victim could experience month to month for two cycles of violence are plotted, as well as the behaviors of the probable states of loss of control that the perpetrator would have during the next twelve months. Based on the results obtained, we generated a help table of indicators that could be used by victim assistance centers and/or health experts for decision-making schemes.
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Everett, R. A., A. M. Packer, and Y. Kuang. "Can Mathematical Models Predict the Outcomes of Prostate Cancer Patients Undergoing Intermittent Androgen Deprivation Therapy?" Biophysical Reviews and Letters 09, no. 02 (June 2014): 173–91. http://dx.doi.org/10.1142/s1793048014300023.

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Androgen deprivation therapy is a common treatment for advanced or metastatic prostate cancer. Like the normal prostate, most tumors depend on androgens for proliferation and survival but often develop treatment resistance. Hormonal treatment causes many undesirable side effects which significantly decrease the quality of life for patients. Intermittently applying androgen deprivation in cycles reduces the total duration with these negative effects and may reduce selective pressure for resistance. We extend an existing model which used measurements of patient testosterone levels to accurately fit measured serum prostate specific antigen (PSA) levels. We test the model's predictive accuracy, using only a subset of the data to find parameter values. The results are compared with those of an existing piecewise linear model which does not use testosterone as an input. Since actual treatment protocol is to re-apply therapy when PSA levels recover beyond some threshold value, we develop a second method for predicting the PSA levels. Based on a small set of data from seven patients, our results showed that the piecewise linear model produced slightly more accurate results while the two predictive methods are comparable. This suggests that a simpler model may be more beneficial for a predictive use compared to a more biologically insightful model, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting. Nevertheless, both models are an important step in this direction.
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Tedeschi, Luis O. "1 Assessing the predictive adequacy of simple and complex mathematical models." Journal of Animal Science 97, Supplement_3 (December 2019): 24. http://dx.doi.org/10.1093/jas/skz258.046.

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Abstract The establishment of credibility for a mathematical model’s (MM) predictive ability is an essential component for improving the MM because it stimulates the evolutionary thinking (i.e., the next generation of the model) of mental conceptualizations, assumptions, and boundaries of the MM. Its predictive adequacy is commonly assessed through its ability to precisely or accurately predict observed (real) values. The precision component measures how closely the model predicted values are of each other or whether a defined pattern of predictions exists. The accuracy component, on the other hand, measures how closely the average of the model predicted values are to the actual (true) average. Many statistics exist to determine precision and accuracy of MM such as mean bias, resistant coefficient of determination, coefficient of determination, modeling efficiency, concordance correlation coefficient (CCC), the mean square error of prediction, Kleijnen’s statistic (regression of the difference between predicted and observed on their sum), and Altman and Bland’s limits of agreement statistics among many more. However, for complex models that use skewed data or repeated data in which the data is not independent (e.g., multiple measurements on the same subject), simple statistics may not suffice. For instance, four methods to compute CCC exist (moment, variance components, U-statistics, and generalized estimating equations—GEE), but only the last two methods are resilient to lightly skewed data. Another type of complexity arises when meta-analytical approaches are used at the model development phase or the model evaluation phase. In general, meta-analytical approaches remove errors (i.e., variation) associated with random variables that are believed to be known. Under these circumstances, MM tends to overperform (i.e., they have greater predictive adequacy) and their future performance may be deceitful when trying to forecast at scenarios in which the random variable(s) is(are) indeterminable or unquantifiable.
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García, B., G. Rubio, C. Santamaría, J. L. Pontones, C. D. Vera, and J. F. Jimenez. "A predictive mathematical model in the recurrence of bladder cancer." Mathematical and Computer Modelling 42, no. 5-6 (September 2005): 621–34. http://dx.doi.org/10.1016/j.mcm.2004.05.013.

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Kesseler, Kevin J., Michael L. Blinov, Timothy C. Elston, William K. Kaufmann, and Dennis A. Simpson. "A predictive mathematical model of the DNA damage G2 checkpoint." Journal of Theoretical Biology 320 (March 2013): 159–69. http://dx.doi.org/10.1016/j.jtbi.2012.12.011.

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Deepa, N., K. Ganesan, and Balaji Sethuramasamyraja. "Predictive mathematical model for solving multi-criteria decision-making problems." Neural Computing and Applications 31, no. 10 (April 27, 2018): 6733–46. http://dx.doi.org/10.1007/s00521-018-3505-2.

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Tedeschi, Luís Orlindo, Danny Gene Fox, Roberto Daniel Sainz, Luís Gustavo Barioni, Sérgio Raposo de Medeiros, and Celso Boin. "Mathematical models in ruminant nutrition." Scientia Agricola 62, no. 1 (January 2005): 76–91. http://dx.doi.org/10.1590/s0103-90162005000100015.

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Mathematical models can be used to improve performance, reduce cost of production, and reduce nutrient excretion by accounting for more of the variation in predicting requirements and feed utilization in each unique production situation. Mathematical models can be classified into five or more categories based on their nature and behavior. Determining the appropriate level of aggregation of equations is a major problem in formulating models. The most critical step is to describe the purpose of the model and then to determine the appropriate mix of empirical and mechanistic representations of physiological functions, given development and evaluation dataset availability, inputs typically available and the benefits versus the risks of use associated with increased sensitivity. We discussed five major feeding systems used around the world. They share common concepts of energy and nutrient requirement and supply by feeds, but differ in structure and application of the concepts. Animal models are used for a variety of purposes, including the simple description of observations, prediction of responses to management, and explanation of biological mechanisms. Depending upon the objectives, a number of different approaches may be used, including classical algebraic equations, predictive empirical relationships, and dynamic, mechanistic models. The latter offer the best opportunity to make full use of the growing body of knowledge regarding animal biology. Continuing development of these types of models and computer technology and software for their implementation holds great promise for improvements in the effectiveness with which fundamental knowledge of animal function can be applied to improve animal agriculture and reduce its impact on the environment.
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Amry, Zul. "Bayesian Estimate of Parameters for ARMA Model Forecasting." Tatra Mountains Mathematical Publications 75, no. 1 (April 1, 2020): 23–32. http://dx.doi.org/10.2478/tmmp-2020-0002.

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AbstractThis paper presents a Bayesian approach to finding the Bayes estimator of parameters for ARMA model forecasting under normal-gamma prior assumption with a quadratic loss function in mathematical expression. Obtaining the conditional posterior predictive density is based on the normal-gamma prior and the conditional predictive density, whereas its marginal conditional posterior predictive density is obtained using the conditional posterior predictive density. Furthermore, the Bayes estimator of parameters is derived from the marginal conditional posterior predictive density.
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Dissertations / Theses on the topic "Predictive mathematical model"

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Lee, Kai-Tien. "Predictive model for plume opacity." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/53886.

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In recent years, as control systems for boiler emissions have been upgraded, some utility sources have experienced increased plume opacity. Cases of plume opacity exceeding in-stack opacity are due to 1) the aerosol formed by condensation of primary sulfuric acid and water vapor onto polydisperse plume particles and 2) the presence of fine particles which grow into the visual size range by heterogeneous condensation and coagulation processes as the plume is cooled and diluted by mixing with the ambient air. In order to better understand the factors leading up to acid plume formation, a computer simulation model has been developed. This plume opacity model has been utilized to simulate sulfuric acid aerosol formation and growth. These processes result from homogeneous nucleation, condensation and coagulation which substantially increase the concentration of submicrometer sized aerosols. These phenomena bring about significant increases in plume opacity. Theoretical relationships have been derived and transformed into 21 computer model to predict plume opacity at various downwind distances resulting from pulverized coal combustion operations. This model consists of relatively independent components-such as an optics module, a bimodal particle size distribution module, a polydisperse coagulation module, a vapor condensation and nucleation module and a plume dispersion module-which are linked together to relate specific flue gas emissions and meterological conditions to plume opacity. This unique, near-stack, plume-opacity-model approach provides an excellent tool for understanding and dealing with such complex issues as: • increasing plume opacity observed for emissions containing sulfuric acid aerosols, • explaining the correlation between primary particle size distribution and light—scattering effects, • predicting the opacity level resulting from combustion of various coal types, • predicting control equipment effects on plume opacity.
Ph. D.
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Buerger, Johannes Albert. "Fast model predictive control." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:6e296415-f02c-4bc2-b171-3bee80fc081a.

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This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.
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Yang, Xiaoke. "Fault-tolerant predictive control : a Gaussian process model based approach." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708784.

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Trapp, Donald R. "The Development of a Predictive Model of Pretrial Misconduct." PDXScholar, 1992. https://pdxscholar.library.pdx.edu/open_access_etds/4574.

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The problem of jail overcrowding has forced corrections officials and jail administrators to examine ways in which to better manage available jail space. Pretrial release and detention policies have been a target of this examination as pretrial defendants typically account for 50% of a jail's population. Standards for pretrial release exist, but their administration varies by jurisdiction. The impact of jail overcrowding on pretrial release policies has been to decrease the time available to render a decision. Recent efforts to standardize pretrial release standards in Oregon have not addressed the issue of expediency. The current study examines pretrial misconduct (failure to appear in court and rearrest) with regard to information that is available to jail personnel and release office personnel at the time of arrest, with the specific intent to develop a predictive model of pretrial misconduct that will function as an initial risk assessment. Six hundred defendants arrested in Washington County, Oregon during 1991 served as subjects. The results indicated that 90.9% of all defendants arrested are released pending trial/ and that 22.7% of those released engaged in pretrial misconduct. The results of the loglinear model-building indicated that the variables prior failure-to-appears/ employment, and age were the best predictors of pretrial misconduct. The construction sample (n = 395) accurately predicted 94.5% of the observed pretrial misconduct compared to 90.7% for the validation sample (n = 150). The loglinear analysis yielded 16 typologies (based on the variables included in the model) by which defendants could be ranked as to their risk of pretrial misconduct. Spearman Rank Order coefficents for the construction and validation samples were .847 and .626 respectively. Data were also collected on detained subjects. A Chi-Square test using detained with released ?Ubjects by typology indicated that the categories are not independent (p < .01). Further examination indicated that the detained subjects did represent higher risks of pretrial misconduct as estimated by the typologies. The results also indicated that defendants currently on probation or parole were more likely to detained than other defendants. The results do not reject the assumptions by Sturz {1962), whose Manhattan Bail Project is the basis for pretrial release, that persons with strong ties to the community may pose the least risk of pretrial misconduct. The results also found sex and ethnic differences with regard to pretrial misconduct. The sex differences may have been confounded by age and crime type; however, the ethnic differences may reflect a systemic inability to communicate with Hispanic offenders.
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Rogalsky, Dennis Wayne. "Quantifying plant model parameter effects on controller performance /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/9843.

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Wu, Sha. "Mathematical Model of Glucose-Insulin Metabolism and Model Predictive Glycemic Control for Critically Ill Patients Considering Time Variability of Insulin Sensitivity." Kyoto University, 2020. http://hdl.handle.net/2433/259047.

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Lakhanpal, Chetan. "Mathematical modelling of applied heat transfer in temperature sensitive packaging systems. Design, development and validation of a heat transfer model using lumped system approach that predicts the performance of cold chain packaging systems under dynamically changing environmental thermal conditions." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/5776.

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Development of temperature controlled packaging (TCP) systems involves a significant lead-time and cost as a result of the large number of tests that are carried out to understand system performance in different internal and external conditions. This MPhil project aims at solving this problem through the development of a transient spreadsheet based model using lumped system approach that predicts the performance of packaging systems under a wide range of internal configurations and dynamically changing environmental thermal conditions. Experimental tests are conducted with the aim of validating the predictive model. Testing includes monitoring system temperature in a wide range of internal configurations and external thermal environments. A good comparison is seen between experimental and model predicted results; increasing the mass of the chilled phase change material (PCM) in a system reduces the damping in product performance thereby reducing the product fluctuations or amplitude of the product performance curve. Results show that the thermal mathematical model predicts duration to failure within an accuracy of +/- 15% for all conditions considered.
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Mirzaei, Hamid Reza. "Using variation in cattle growth to develop a predictive model of carcass quality /." Title page, table of contents and abstract only, 2004. http://web4.library.adelaide.edu.au/theses/09PH/09phm677.pdf.

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Zvaigzne, Anita Ilze. "Thermochemical investigations of crystalline solutes in non-electrolyte solutions: Mathematical representation of solubility data and the development of predictive solubility equations in systems with specific and non-specific interactions." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc28369/.

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Understanding the thermodynamic properties of multicomponent mixtures is of critical importance in many chemical and industrial applications. Experimental measurements become progressively difficult as the number of solution components increases -- producing the need for predictive models. Problems in development of predictive models arise if the mixture has one or more components that interact through molecular complexation or association. Experimental solubilities of anthracene and pyrene dissolved in binary systems containing one or more alcohols were measured in order to address this problem. Alcohols examined in this study were: 1-propanol, 2-propanol, 1-butanol, 2-butanol, 2-methyl-1-propanol, 3-methyl-1-butanol, and 1-octanol. In binary solvent mixtures containing only a single self-associating alcoholic solvent, the alkane cosolvents studied were: n-hexane, n-heptane, n-octane, 2,2,4-trimethylpentane, cyclohexane, methylcyclohexane, tert-butylcyclohexane. Predictive solubility equations were developed using mobile order theory. This approach differs from classical solution models by representing hydrogen bonding with a probability term rather than with expressions derived from stepwise equilibria or expressions to represent hypothetical solution aggregates. Results were compared with the predicted solubilities found from using expressions developed using the Kretschmer-Wiebe and Mecke-Kempter approaches for modeling associated solutions. It was found that the mobile order approach provided reasonably accurate predictions for the solute solubilities in the systems studied. The limitations and applications for mathematical methods of representing experimental isothermal solubility data were also studied for 72 systems. Two possible descriptive forms for this mathematical representation were suggested based on the various nearly ideal binary solvent (NIBS) and modified Wilson models.
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Eifert, Joseph D. "Predictive modeling of the aerobic growth of Staphylococcus aureus 196E using a nonlinear model and response surface analysis." Diss., Virginia Tech, 1994. http://hdl.handle.net/10919/27970.

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Pathogenic bacteria in foods are affected by several factors which may interact to enhance or inhibit microbial growth. Staphylococcus aureus 196E was inoculated into Brain Heart Infusion broth formulated with either 0.5, 4.5 or 8.5% NaCI, adjusted to pH 5.0, 6.0 or 7.0, and incubated aerobically at 12, 20 or 28°C. Mathematical models to predict the growth of S. aureus 196E were developed using a modified Gompertz function and response surface methodology. Each predictive equation required the estimation of only 23 parameters with a biological meaning. These models determined the significance of time, incubation temperature, sodium chloride (NaCI) concentration, and either pH or the logₑ of the undissociated acid concentration and any interactions on growth kinetics. Separate models were developed for the cases where pH was altered with either acetic acid, acetic acid plus sodium hydroxide, lactic acid and hydrochloric acid. All models adequately predicted the log growth of S. aureus 196E. Several interactive relationships between the independent variables upon population growth were significant. Predicted responses to multiple factor interactions were displayed with three-dimensional and contour plots. One model developed from a smaller subset of the growth data demonstrated that models could be produced with much less data collection. Generally, predictions of growth showed that acetic acid was more inhibitory to growth than lactic and hydrochloric acids. Furthermore, predicted and observed growth was slower or reduced when the undissociated acetic acid concentration was elevated at a specific pH. This methodology can provide important information to food scientists about the growth kinetics of microorganisms and prediction ranges or confidence intervals for growth parameters. Consequently, the effects of food formulations and storage conditions on the growth kinetics of foodborne pathogens or spoilage microorganisms could be predicted.
Ph. D.
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Books on the topic "Predictive mathematical model"

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Vasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014071.

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Presents the current status in modelling of metallurgical processes considered by the model the mathematical model used in the description of the processes of copper production and their classification. Set out a system of methods and models in the field of mathematical modeling of technological processes, including balance sheet, statistics, optimization models, forecasting models and predictive models. For specific technological processes are developed: the model of the balance of the cycle of pyrometallurgical production of copper, polynomial model for prediction of matte composition on the basis of the passive experiment, predictive model of quantitative estimation of the copper content in the matte based on fuzzy logic. Of interest to students, postgraduates, teachers of technical universities, engineers and research workers who use mathematical methods for processing of data of laboratory and industrial experiments.
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Chen, J. Quality assurance of multi-media model for predictive screening tests. Washington, DC: U.S. Environmental Protection Agency, Office of Research and Development, 1999.

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Evernden, J. F. Predictive model for important ground motion parameters associated with large and great earthquakes. Washington, DC: U.S. Geological Survey, 1988.

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Evernden, J. F. Predictive model for important ground motion parameters associated with large and great earthquakes. [Washington]: U.S. G.P.O., 1988.

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Schloss, Alan J. A predictive model for estimating maximum summer stream temperatures in western Oregon. Springfield VA: Eugene, OR, 1985.

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Bradley, John E. An archaeological survey and predictive model of selected areas of Utah's Cisco Desert. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.

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Bradley, John E. An archaeological survey and predictive model of selected areas of Utah's Cisco Desert. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.

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Bradley, John E. An archaeological survey and predictive model of selected areas of Utah's Cisco Desert. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.

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Bradley, John E. An archaeological survey and predictive model of selected areas of Utah's Cisco Desert. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.

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Tipps, Betsy L. The Tar Sands Project: An inventory and predictive model for central and southern Utah. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1988.

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Book chapters on the topic "Predictive mathematical model"

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Albin Rajasingham, Thivaharan. "Mathematical Fundamentals of Optimization." In Nonlinear Model Predictive Control of Combustion Engines, 37–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68010-7_3.

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Ocampo-Martinez, Carlos. "Principles of the Mathematical Modelling of Sewer Networks." In Model Predictive Control of Wastewater Systems, 41–58. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-353-4_3.

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Nakayama, Hirotaka, Yeboon Yun, and Masakazu Shirakawa. "Multi-objective Model Predictive Control." In Lecture Notes in Economics and Mathematical Systems, 277–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04045-0_24.

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Chakrabarti, Arijit, and Tapas Samanta. "Asymptotic optimality of a cross-validatory predictive approach to linear model selection." In Institute of Mathematical Statistics Collections, 138–54. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/074921708000000110.

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Sánchez, Alberto, and María S. Pérez. "A Mathematical Predictive Model for an Autonomic System to Grid Environments." In Computational Science and Its Applications – ICCSA 2005, 109–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424857_12.

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Skelton, Andrew, and Allan R. Willms. "To a Predictive Model of Pathogen Die-off in Soil Following Manure Application." In Mathematical and Computational Approaches in Advancing Modern Science and Engineering, 309–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30379-6_29.

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Carson, E. R. "The Role of Dynamic Mathematical Models." In The Future of Predictive Safety Evaluation, 211–15. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4139-7_16.

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Moss, Gary P., Darren R. Gullick, and Simon C. Wilkinson. "Mathematical Treatments and Early Models of Skin Permeability." In Predictive Methods in Percutaneous Absorption, 43–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47371-9_3.

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Iverson, Richard M. "How Should Mathematical Models of Geomorphic Processes be Judged?" In Prediction in Geomorphology, 83–94. Washington, D. C: American Geophysical Union, 2013. http://dx.doi.org/10.1029/135gm07.

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Jehlička, Vladimír. "Mathematical Models of Multivariable Systems." In Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, 317–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07401-6_31.

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Conference papers on the topic "Predictive mathematical model"

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Kostial, Imrich, Dusan Nascak, Jan Kerekanic, and Peter Kosinar. "Mathematical model for the rotary furnace predictive control." In 2013 14th International Carpathian Control Conference (ICCC). IEEE, 2013. http://dx.doi.org/10.1109/carpathiancc.2013.6560529.

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Nagy, Endre. "Model predictive control: A new approach." In ICNPAA 2016 WORLD CONGRESS: 11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences. Author(s), 2017. http://dx.doi.org/10.1063/1.4972697.

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Gruene, Lars, and Simon Pirkelmann. "Model Predictive Control of a Time-Varying Convection-Diffusion Equation with State Constraints." In 9th Vienna Conference on Mathematical Modelling. ARGESIM Publisher Vienna, 2018. http://dx.doi.org/10.11128/arep.55.a55263.

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Procopio, Anna, Mariaconcetta Bilotta, Alessio Merola, Francesco Amato, Carlo Cosentino, Salvatore De Rosa, Caterina Covello, Jolanda Sabatino, Alessia De Luca, and Ciro Indolfi. "Predictive mathematical model of cardiac troponin release following acute myocardial infarction." In 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2017. http://dx.doi.org/10.1109/icnsc.2017.8000166.

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Kannan, Somasundar, Seyed Amin Sajadi Alamdari, Jan Dentler, Miguel A. Olivares-Mendez, and Holger Voos. "Model predictive control for cooperative control of space robots." In ICNPAA 2016 WORLD CONGRESS: 11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences. Author(s), 2017. http://dx.doi.org/10.1063/1.4972660.

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Lu, Hanxiao, Jian Li, Ronghai Qu, Donglin Ye, Yang Lu, and Rui Zhang. "Post-fault model predictive control of asymmetrical six-phase permanent magnet machine with improved mathematical model." In 2017 IEEE International Electric Machines and Drives Conference (IEMDC). IEEE, 2017. http://dx.doi.org/10.1109/iemdc.2017.8002332.

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Loungthongkam, Auttapoom, and Chana Raksiri. "A Development of Mathematical Model for Predictive of The Standard Uncertainty of Robot Arm." In 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea49774.2020.9101979.

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Jaenisch, Holger M., James W. Handley, and Michael L. Hicklen. "Data model predictive control as a new mathematical framework for simulation and VV&A." In Defense and Security Symposium, edited by Kevin L. Priddy and Emre Ertin. SPIE, 2006. http://dx.doi.org/10.1117/12.666466.

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Syaifudin, Wawan Hafid, and Endah R. M. Putri. "The application of model predictive control on stock portfolio optimization without loan." In PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5139166.

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Zareifard, Mohammad Taghi, and Andrey V. Savkin. "Model predictive control for wind power generation smoothing with controlled battery storage based on a nonlinear battery mathematical model." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244572.

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Reports on the topic "Predictive mathematical model"

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Levin, Sheldon G., and J. T. Klopcic. Mathematical Models for Prediction of Neuropsychiatric and Other Non-Battle Casualties in High Intensity Combat. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada171283.

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