Overview
- Introduces to a wide audience symbolic regression methods to find functions and laws in a form familiar with engineers
- Offers solutions in control automation, and also in the design of completely different optimal structures in all fields
- For control system engineers and machine learning specialists; also mathematicians, optimization specialists, students
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Table of contents (5 chapters)
Keywords
About this book
For specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints scientists with the methods of automatic construction of control systems.For specialists in the fieldof machine learning, the book opens up a new, much broader direction than neural networks: methods of symbolic regression. This book makes it easy to master this new area in machine learning and apply this approach everywhere neural networks are used. For mathematicians, the book opens up a new approach to the construction of numerical methods for obtaining analytical solutions to unsolvable problems; for example, numerical analytical solutions of algebraic equations, differential equations, non-trivial integrals, etc.
For specialists in the field of artificial intelligence, the book offers a machine way to solve problems, framed in the form of analytical relationships.
Authors and Affiliations
About the authors
Dr. Shmalko is a former student and follower of Prof. Diveev, received the B.S. and M.S. degrees in Computer Science and Cybernetics from RUDN University, Engineering Dept. and the Ph.D. degree from Dorodnicyn Computing Center of the Russian Academy of Sciences, Moscow, Russia, in 2009. From 2007 to 2010, she was with IBM East Europe/Asia. Since 2010, she is a Senior researcher with the Computing Center of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences.
The authors’ current research interests are computational methods in control, symbolic regression and evolutionary computation with applications to model identification, optimization and control system synthesis. The authors conduct theoretical research and implement applied tasks on the basis of the Robotics Center of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences.
Bibliographic Information
Book Title: Machine Learning Control by Symbolic Regression
Authors: Askhat Diveev, Elizaveta Shmalko
DOI: https://doi.org/10.1007/978-3-030-83213-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-83212-4Published: 24 October 2021
Softcover ISBN: 978-3-030-83215-5Published: 25 October 2022
eBook ISBN: 978-3-030-83213-1Published: 23 October 2021
Edition Number: 1
Number of Pages: IX, 155
Number of Illustrations: 36 b/w illustrations, 19 illustrations in colour
Topics: Machine Learning, Artificial Intelligence, Systems Theory, Control, Control and Systems Theory, Control, Robotics, Mechatronics, Multiagent Systems