Name: Classical and Modern Optimization Techniques Applied to Control and Modeling (en Inglés)
SKU: 64086054
Price: 355900.00 CLP
Availability: InStock
Author: Radu-Emil Precup; Raul-Cristian Roman; Elena-Lorena Hedrea; Alexandra-Iulia Szedlak-Stinean; Iuliu Alexandru Zamfirache
ISBN: 9781032785110

portada Classical and Modern Optimization Techniques Applied to Control and Modeling (en Inglés)

Classical and Modern Optimization Techniques Applied to Control and Modeling (en Inglés)

Formato

Libro Físico

Editorial

Crc Press

Autor

Radu-Emil Precup; Raul-Cristian Roman; Elena-Lorena Hedrea; Alexandra-Iulia Szedlak-Stinean; Iuliu Alexandru Zamfirache

Idioma

Inglés

N° páginas

362

Encuadernación

Tapa Dura

ISBN13

9781032785110

N° edición

Classical and Modern Optimization Techniques Applied to Control and Modeling (en Inglés)

Radu-Emil Precup; Raul-Cristian Roman; Elena-Lorena Hedrea; Alexandra-Iulia Szedlak-Stinean; Iuliu Alexandru Zamfirache (Autor) · Crc Press · Tapa Dura

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Reseña del libro "Classical and Modern Optimization Techniques Applied to Control and Modeling (en Inglés)"

The book presents a detailed and unified treatment of the theory and applications of optimization applied to control and modeling, focusing on nature-inspired optimization algorithms to optimally tune the parameters of linear and nonlinear controllers and models, with emphasis on tower crane systems and other representative applications.Classical and Modern Optimization Techniques Applied to Control and Modeling combines classical and modern approaches to optimization, based on the authors' experience in the field, and presents in a unified structure the essential aspects of optimization in control and modeling from a control engineer's point of view. It covers linear and nonlinear controllers, and neural networks based on reinforcement learning are considered and analyzed because of the need to reduce the complexity of the controllers and their design so that they can be practical to implement as low-cost automation solutions. The chapters are designed to quickly make the concepts of optimization, control, reinforcement learning, and neural networks understandable to readers with limited experience.This book is intended for a broad audience, including undergraduate and graduate students, engineers (designers, practitioners, and researchers), and anyone facing challenging control problems.