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Книги з теми "SOFTWARE PREDICTION MODELS"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

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

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2

Ramamurthy, Karthikeyan N. MATLAB software for the code excited linear prediction algorithm: The Federal Standard, 1016. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2010.

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3

Metrics for process models: Empirical foundations of verification, error prediction, and guidelines for correctness. Berlin: Springer, 2008.

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4

Matthew, O'Keefe, Kerr Christopher, United States. Dept. of Energy. Office of Biological and Environmental Research., and Goddard Space Flight Center, eds. Second International Workshop on Software Engineering and Code Design in Parallel Meteorological and Oceanographic Applications: Proceedings of a workshop sponsored by the U.S. Department of Energy, Office of Biological and Environmental Research; the Department of Defense, High Performance Computing and Modernization Office; and the NASA Goddard Space Flight Center, Seasonal-to-Interannual Prediction Project, and held at the Camelback Inn, Scottsdale, Arizona, June 15-18, 1998. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1998.

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5

Dennison, Thomas E. Fitting and prediction uncertainty for a software reliability model. Monterey, Calif: Naval Postgraduate School, 1992.

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6

Fernandez-Camacho, Eduardo. Model Predictive Control in the Process Industry. London: Springer London, 1995.

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7

Ahmad, Anees. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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8

Zhen, Feng, and United States. National Aeronautics and Space Administration., eds. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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9

Chen, Feng, and United States. National Aeronautics and Space Administration., eds. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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10

Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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Анотація:

The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects). The second edition is supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms. Meets the requirements of Federal state educational standards of higher education of the last generation. For students of economic specialties, specialists, and graduate students.

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11

United States. National Aeronautics and Space Administration., ed. An RL10A-3-3A rocket engine model using the rocket engine transient simulator (ROCETS) software. [Washington, D.C.]: National Aeronautics and Space Administration, 1993.

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12

Model Predictive Critical Soft-Switching Enabling High-Performance Software-Defined Power Electronics: Converter Configuration, Efficiency, and Redundancy. [New York, N.Y.?]: [publisher not identified], 2022.

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13

Ijsmi, Editor. Forecasting Models - an Overview with the Help of R Software: Time Series Prediction - Past, Present and Future. Independently Published, 2019.

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14

Mendling, Jan. Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness. Springer London, Limited, 2008.

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15

Sallih, M. M. A study of models for predicting computer software reliability. 1986.

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16

Martinie, Célia, Philippe Palanque, and Camille Fayollas. Performance Evaluation of Interactive Systems with Interactive Cooperative Objects Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0010.

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Анотація:

Arguments to support validity of most contributions in the field of human–computer interaction are based on detailed results of empirical studies involving cohorts of tested users confronted with a set of tasks performed on a prototype version of an interactive system. This chapter presents how the Interactive Cooperative Objects (ICO) formal models of the entire interactive system can support predictive and summative performance evaluation activities by exploiting the models. Predictive performance evaluation is supported by ICO formal models of interactive systems enriched with perceptive, cognitive, and motoric information about the users. Summative usability evaluation is addressed at the level of the software system, which is able to exhaustively log all the user actions performed on the interactive system The articulation of these two evaluation approaches is demonstrated on a case study from the avionics domain with a step-by-step tutorial on how to apply the approach.

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17

Fontaine, Alan. Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python. Packt Publishing, 2018.

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18

An RL10A-3-3A rocket engine model using the rocket engine transient simulator (ROCETS) software. [Washington, D.C.]: National Aeronautics and Space Administration, 1993.

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19

Nozière, Pierre. INRA feeding system for ruminants. Edited by Daniel Sauvant and Luc Delaby. Wageningen Academic Publishers, 2018. http://dx.doi.org/10.3920/978-90-8686-872-8.

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The INRA Feeding System for Ruminants has been renewed to better address emerging challenges for animal nutrition: prevision of productive responses, product quality, animal health and emissions to the environment, in a larger extent of breeding contexts. The new system is mainly built from meta-analyses of large data bases, and modelling. The dietary supply model accounts for digestive interactions and flows of individual nutrients, so that feed values depend on the final ration. Animal requirements account for variability in metabolic efficiency. Various productive and non-productive animal responses to diets are quantified. This book presents the whole system for dairy and meat, large and small ruminant production, including specificities for tropical and Mediterranean areas. The first two sections present biological concepts and equations (with their field of application and statistical accuracy) used to predict intake (including at grazing) and nutrient supply (Section 1), animal’s requirements and multiple responses to diets (Section 2). They apply to net energy, metabolisable protein and amino acids, water, minerals and vitamins. Section 3 presents the use of concepts and equations in rationing with two purposes: (1) diet calculation for a given performance objective; and (2) prediction of the multiple responses of animal to diet changes. Section 4 displays the tables of feed values, and their prevision. All the equations and concepts are embedded in the fifth version of INRAtion® software for practical use.

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20

Belia, Evangelia, Lorenzo Benedetti, Bruce Johnson, Sudhir Murthy, Marc Neumann, Peter Vanrolleghem, and Stefan Weijers, eds. Uncertainty in Wastewater Treatment Design and Operation. IWA Publishing, 2021. http://dx.doi.org/10.2166/9781780401034.

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Uncertainty in Wastewater Treatment Design and Operation aims to facilitate the transition of the wastewater profession to the probabilistic use of simulators with the associated benefits of being better able to take advantage of opportunities and manage risk. There is a paradigm shift taking place in the design and operation of treatment plants in the water industry. The market is currently in transition to use modelling and simulation while still using conventional heuristic guidelines (safety factors). Key reasons for transition include: wastewater treatment simulation software advancements; stricter effluent requirements that cannot be designed for using traditional approaches, and increased pressure for more efficient designs (including energy efficiency, greenhouse gas emission control). There is increasing consensus among wastewater professionals that the performance of plants and the predictive power of their models (degree of uncertainty) is a critical component of plant design and operation. However, models and simulators used by designers and operators do not incorporate methods for the evaluation of uncertainty associated with each design. Thus, engineers often combine safety factors with simulation results in an arbitrary way based on designer ‘experience’. Furthermore, there is not an accepted methodology (outside modelling) that translates uncertainty to assumed opportunity or risk and how it is distributed among consultants/contractors and owners. Uncertainty in Wastewater Treatment Design and Operation documents how uncertainty, opportunity and risk are currently handled in the wastewater treatment practice by consultants, utilities and regulators. The book provides a useful set of terms and definitions relating to uncertainty and promotes an understanding of the issues and terms involved. It identifies the sources of uncertainty in different project phases and presents a critical review of the available methods. Real-world examples are selected to illustrate where and when sources of uncertainty are introduced and how models are implemented and used in design projects and in operational optimisation. Uncertainty in Wastewater Treatment Design and Operation defines the developments required to provide improved procedures and tools to implement uncertainty and risk evaluations in projects. It is a vital reference for utilities, regulators, consultants, and trained management dealing with certainty, opportunity and risk in wastewater treatment. ISBN: 9781780401027 (Paperback) ISBN: 9781780401034 (eBook) ISBN: 9781789062601 (ePub)

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