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Solving Optimization Problems with the Heuristic Kalman Algorithm: New Stochastic Methods: 212 (Springer Optimization and Its Applications, 212) - Tapa dura

Toscano, Rosario

 
9783031524585: Solving Optimization Problems with the Heuristic Kalman Algorithm: New Stochastic Methods: 212 (Springer Optimization and Its Applications, 212)
Tapa dura
ISBN 10: 3031524586 ISBN 13: 9783031524585
Editorial: Springer, 2024

Sinopsis

This text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering design, namely non-convex optimization problems. 

The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems.

The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more. 

The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design. 

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Acerca del autor

​Rosario Toscano was born in Catania, Italy. He received his masters degree with specialization in control from the Institut National des Sciences Appliquées de Lyon in 1996. He received the Ph.D. degree from the Ecole Centrale de Lyon in 2000. He received the HDR degree (Habilitation to Direct Research) from the University Jean Monnet of Saint-Etienne in 2007. He is currently full professor at the Ecole Nationale d'Ingénieurs de Saint-Etienne and Ecole Centrale de Lyon (ENISE-ECL). His research interests include: structured controllers, robust control, stochastic optimization methods, dynamic reliability, fault detection, multimodel approach applied to diagnosis and control, fretting wear of mechanical surfaces and sensorial design of products.

De la contraportada

This text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering design, namely non-convex optimization problems. 


The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems.

The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more. 

The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design. 

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2024
Idioma
Inglés
ISBN 10 
3031524586
ISBN 13 
9783031524585
Encuadernación
Tapa dura
Número de páginas
308
Contacto del fabricante
no disponible
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