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Classical and Modern Optimization Techniques Applied to Control and Modeling by Radu-Emil Precup at Abbey's Bookshop,
Classical and Modern Optimization Techniques Applied to Control and Modeling
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Radu-Emil Precup Raul-Cristian Roman

Classical and Modern Optimization Techniques Applied to Control and Modeling

Radu-Emil Precup,  Raul-Cristian Roman,  Elena-Lorena Hedrea ,  Alexandra-Iulia Szedlak-Stinean

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English
CRC Press
24 March 2025
Power generation & distribution; Automatic control engineering
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.
By:   , , , ,
Imprint:   CRC Press
Country of Publication:   United Kingdom
Dimensions:   Height: 234mm,  Width: 156mm, 
Weight:   830g
ISBN:   9781032785110
ISBN 10:   103278511X
Pages:   354
Publication Date:  
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
Chapter 1- Introduction Chapter 2- One-step Optimization Chapter 3- Discrete-time Optimization Chapter 4- Numerical Solving of Optimization Problems Chapter 5- Metaheuristic Optimization Algorithms Chapter 6- Optimization Algorithms in Artificial Neural Network Training Chapter 7- Introduction to Data Mining Chapter 8- Reinforcement Learning Applied to Optimal Control

Dr. Radu‐Emil Precup is a professor with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania, and Senior Researcher (CS I) and the Head of the Data Science and Engineering Laboratory of the Center for Fundamental and Advanced Technical Research, Romanian Academy – Timisoara Branch, Romania. Dr. Raul‐Cristian Roman is a lecturer with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania. He received a PhD in systems engineering in 2018 from Politehnica University of Timisoara, Timisoara, Romania. Dr. Elena-Lorena Hedrea is an assistant lecturer with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania. Dr. Alexandra-Iulia Szedlak-Stinean is a lecturer with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania. Iuliu Alexandru Zamfirache is a PhD student with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania.

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