Adaptive methods of prediction of technological process parameters

Description: The discipline studies adaptive forecasting methods that allow building self-adjusting mathematical models that are able to respond quickly to changing conditions by taking into account the result of the forecast made in the previous step and taking into account the various informational values of the series. The use of adaptive methods of short-term forecasting of the state of working technical systems is considered.

Amount of credits: 6

Пререквизиты:

• Automation of Engineering Systems

Course Workload:

Component: University component

Cycle: Profiling disciplines

Goal

• mastering and understanding forecasting methods and techniques that make it possible to predict changes in technological process parameters with high accuracy.

Objective

• studying the basic concepts and principles of adaptive forecasting of technological process parameters, advantages and limitations of adaptive methods for predicting technological process parameters;
• mastering adaptive forecasting methods, including autoregression, moving average, exponential smoothing and others;
• mastering algorithms and software tools for adaptive forecasting;
• acquiring skills in using adaptive forecasting methods using the example of real technological processes;
• developing skills for assessing the quality of forecasts and analyzing results.

Learning outcome: knowledge and understanding

• describe the theoretical foundations of adaptive methods for predicting parameters of technological processes;
• explain the basic concepts and compare models used in adaptive forecasting methods;

Learning outcome: applying knowledge and understanding

• apply various algorithms and forecasting methods to obtain optimal results from the functioning of process automation systems;

Learning outcome: formation of judgments

• analyze large volumes of data, select appropriate statistical models and test their effectiveness;
• critically evaluate the forecasting results obtained and make informed decisions based on these results;

Learning outcome: communicative abilities

• to put into practice skills in organizing research and design work, in team management;

Learning outcome: learning skills or learning abilities

• solve problems of predicting parameters of technological processes based on adaptive methods, using available data and modern tools.

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

• Basics of Adaptive Forecasting
• Time series methods in adaptive forecasting
• Adaptive filtering in process forecasting
• Adaptive neural networks in predicting process parameters
• Adaptive regression in forecasting technological parameters
• Adaptive moving average in process forecasting
• Adaptive exponential smoothing in predicting process parameters
• Adaptive Kalman filter in process forecasting
• Adaptive clustering in predicting technological parameters
• Adaptive control and indication in process forecasting
• Adaptive process optimization in predicting technological parameters
• Adaptive regulation and control in process forecasting
• Adaptive expert systems in predicting technological parameters
• Adaptive Fourier transform in process forecasting
• Применение адаптивных методов прогнозирования в реальных технологических системах

Key reading

• Adaptivnye sistemy upravleniya: uchebnoe posobie / A.R. Gajduk, E.A. Plaksienko; Yuzhnyj federal'nyj universitet. – Rostov-na-Donu; Taganrog: izd-vo YuFU, 2018. – 120s.
• Akulov A.V. Adaptivnye metody prognozirovaniya parametrov tekhnologicheskih processov: uchebnoe posobie. – Moskva: Izdatel'stvo MGTU im. N.E. Baumana, 2012.
• Kocharov A.C., Lineckij A.V., Zemcova E.S. Adaptivnye metody prognozirovaniya parametrov tekhnologicheskih processov: uchebno-metodicheskoe posobie. – Moskva: Izdatel'stvo MEI, 2009.

Further reading

• Zav'yalova I.B. Adaptivnye metody prognozirovaniya parametrov v tekhnologicheskih processah: monografiya. – Moskva: Nauka, 2006.
• Chzhan Ubun, Chzhu Naiken, Huan Gushen. Adaptivnye metody prognozirovaniya parametrov tekhnologicheskih processov: nauchno-prakticheskoe posobie. – Pekin: Izdatel'stvo Naukov, 2016.