Multifactorial models for predicting elements of automation of technological processes

Description: The discipline studies forecasting models, the basic means of expressing modern forecasting models, the requirements for models. It covers issues related to the principles and rules for constructing a predictive model. The classification of forecasting methods is considered. Acquired skills in the development of an adequate predictive model describing the technical processes and production processes, as well as the process of development of scientific research.

Amount of credits: 5

Пререквизиты:

• Nonlinear Systems of Automatic Control

Course Workload:

Component: Component by selection

Cycle: Base disciplines

Goal

• mastering the knowledge and skills to create mathematical models that make it possible to predict various elements and parameters in automated technological processes.

Objective

• studying the basic concepts and methods of multivariate analysis and modeling;
• mastering methods for estimating parameters, diagnosing and optimizing multifactor forecasting models;
• formation of skills in forecasting elements of automated technological processes.

Learning outcome: knowledge and understanding

• Summarize and compare various statistical forecasting methods and their application in the context of process automation

Learning outcome: applying knowledge and understanding

• Formulate the selection of elements of automated technological processes using multifactor forecasting models;

Learning outcome: formation of judgments

• Analyze the influence of various factors on automation processes and build models that take into account the relationship between them, various methods for assessing the accuracy and stability of forecasts, as well as methods for identifying and eliminating possible problems of data distortion.

Learning outcome: communicative abilities

• Evaluate information on forecasting elements of automated technological processes;
• Discuss the results of teamwork and team interaction in solving problems of process automation;

Learning outcome: learning skills or learning abilities

• Demonstrate practical skills in using specialized software and tools to develop and evaluate multivariate models.

Teaching methods

When conducting training sessions, the use of the following educational technologies is provided: - interactive lecture (use of the following active forms of learning: guided (managed) discussion or conversation; moderation; demonstration of slides or educational films; brainstorming; motivational speech); - building scenarios for the development of various situations based on given conditions; - information and communication (for example, classes in a computer class using professional application software packages); - search and research (independent research activities of students during the learning process); - solving educational problems.

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Introduction to multifactor forecasting models for elements of automated technological processes: general information about forecasting models and their application in automated technological processes
• Data collection methods for multifactor forecasting models: various approaches and tools for collecting and processing data to build forecasting models
• Construction of multifactor forecasting models: main stages and approaches to the development of forecasting models based on multiple factors
• Quality assessment and selection of the optimal forecasting model: methods for assessing the accuracy and reliability of multifactor models and their application to select the best forecasting model
• Forecasting methods based on regression models: analysis and forecasting of data using various regression models
• Time series forecasting methods: analysis and forecasting of time series using appropriate methods and models
• Forecasting methods based on artificial neural networks: the use of neural networks to predict elements of automated technological processes
• Forecasting methods based on genetic algorithms: the use of genetic algorithms to build and optimize multifactor forecasting models
• Forecasting methods based on machine learning: the use of machine learning algorithms to predict elements of automated technological processes
• Forecasting methods based on cluster analysis: analysis and grouping of data using cluster methods for subsequent forecasting
• Integration of multifactor forecasting models into automated process control systems: the use of forecasting models to optimize the operation of automated control systems
• Integration of multifactor forecasting models with monitoring and diagnostic systems: the use of forecasting models for early detection of problems and anomalies in automated technological processes
• Application of multifactor forecasting models in industry: examples and practical advice on the use of forecasting models in various industries
• Computational aspects of the use of multifactor forecasting models: optimization of computational processes and selection of suitable methods for effective forecasting
• Trends in the development of multifactor forecasting models in automated technological processes: a review of new approaches and innovations in the field of multifactor forecasting for technological processes

Key reading

• Demin A, Dmitrieva S. Prognozirovanie tekhnicheskih processov metodami matmodelirovaniya. Ch.1. Matematicheskie metody analiticheskogo i komp'yuternogo modelirovaniya dlya prognozirovaniya sostoyaniya tekhnogennoj sredy. - Izdatel'stvo: LAP LAMBERT Academic, 2016 - 196 s.
• Galustov, G. G. Matematicheskoe modelirovanie i prognozirovanie v tekhnicheskih sistemah: Uchebnoe posobie / Galustov G.G., Sedov A.V. - Rostov-na-Donu:Izdatel'stvo YuFU, 2016. - 107 s.

Further reading

• Chen, Y., & Bao, T. (2019). Multi-factor prediction models for automated technological processes: A comprehensive review and performance comparison. Journal of Manufacturing Systems, 52, 155-166.
• Nguyen, T. A., & Wang, S. (2018). A novel multi-factor model for predicting elements of automated technological processes. Expert Systems with Applications, 100, 267-278.
• Li, M., & Dong, C. (2017). Multi-factor modeling and prediction of elements in automated technological processes using machine learning techniques. Computers & Industrial Engineering, 107, 131-142.
• Zhang, Q., & Liu, Y. (2016). Multi-factor prediction models for optimizing automated technological processes in manufacturing. International Journal of Production Research, 54(4), 1131-1143.
• Wang, H., & Luo, Y. (2015). A comparative study of multi-factor prediction models for automated technological processes in the semiconductor industry. Journal of Intelligent Manufacturing, 26(2), 309-322.