Machine Learning Control by Symbolic Regre...

Dekorationsartikel gehören nicht zum Leistungsumfang.

Sprache: Englisch

138,95 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 2-4 Werktage

Kategorien:

Der Artikel ist nicht in deutscher Sprache verfasst.
Die Sprache der Produktbeschreibung und Details kann in Englisch angegeben sein. Sollten Sie Verständnisprobleme haben, wenden Sie sich gerne an uns.

Beschreibung

This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting; the search for new laws in economics, politics, sociology. Accumulating many years of experience in the development and application of numerical methods of symbolic regression to solving control problems, the authors offer new possibilities not only in the field of control automation, but also in the design of completely different optimal structures in many fields.

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.

Über den Autor

Prof., Dr. Diveev is a renowned specialist in the field of control and a leading researcher in Russia in evolutionary computation and symbolic regression. He received the Ph.D. degree in technical science from Bauman Moscow State Technical University, in 1989, and Doctor of Sciences in 2001 in Dorodnitsyn Computing Center of the Russian Academy of Sciences, in 2009 he became a professor. Presently, he works as a Director of Robotic Center of Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences. He is also a Professor at the RUDN University, Engineering Department. He is the author of five books, more than 300 articles. Prof. Diveev is a member of the editorial board of the RUDN journal of Engineering Researches and journal of Instrument Engineering of the Bauman Moscow State Technical University, a general chair of the INTELS Symposium.

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.

Zusammenfassung

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

Inhaltsverzeichnis

1.Introduction.- 2.Mathematical Statements of MLC Problems.- 3.Numerical Solution of Machine Learning Control Problems.- 4.Symbolic Regression Methods.- 5.Examples of MLC Problem Solutions.

Details

Erscheinungsjahr:	2022
Genre:	Informatik , Mathematik , Medizin , Naturwissenschaften , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	ix 155 S. 36 s/w Illustr. 19 farbige Illustr. 155 p. 55 illus. 19 illus. in color.
ISBN-13:	9783030832155
ISBN-10:	3030832155
Sprache:	Englisch
Ausstattung / Beilage:	Paperback
Einband:	Kartoniert / Broschiert
Autor:	Shmalko, Elizaveta Diveev, Askhat
Auflage:	1st ed. 2021
Hersteller:	Springer International Publishing Springer International Publishing AG
Verantwortliche Person für die EU:	Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße:	235 x 155 x 10 mm
Von/Mit:	Elizaveta Shmalko (u. a.)
Erscheinungsdatum:	25.10.2022
Gewicht:	0,265 kg

Artikel-ID: 125330341