Artificial Neural Networks for System Identification and Control
CRC Press (Verlag)
978-1-041-04911-1 (ISBN)
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Offers a practical and accessible guide to ANN-based system identification and control
Blends mathematical insight with real engineering applications
Provides Python-supported examples and visual case studies
Highlights key advances in nonlinear modeling and adaptive control design
Bridges the gap between theory, simulation, and real-world deployment
This book is aimed toward engineers, researchers, and advanced students seeking to apply artificial intelligence to control theory, robotics, and signal processing and to design smarter, more adaptive engineering systems.
Serhat Seker earned the degree at the Electrical Engineering Department, Istanbul Technical University (ITU) and a master’s and PhD degrees at the Electrical Engineering Division, Science and Technology Institute, ITU. He studied the PhD thesis with the Energy Research Centre of the Netherlands (ECN) and worked on signal analysis techniques. He was an Assistant Professor and an Associate Professor with ITU in 1995 and 1996. He worked in industrial signal processing with the Maintenance and Reliability Centre, University of Tennessee, Knoxville, Tennessee, USA, in 1997. He was the Vice Dean of the Electrical and Electronic Engineering Faculty from 2001 to 2004 and the Head of the Electrical Engineering Department from 2004 to 2007. He was also the Dean of the Faculty of Electrical and Electronics from 2013 to 2020. Tahir Cetin Akinci (Senior Member, IEEE) pursued a bachelor’s degree in electrical engineering in 2000, followed by a master’s and PhD degrees in 2005 and 2010, respectively. From 2003 to 2010, he worked as a Research Assistant at Marmara University in Istanbul, Turkey. Dr. Akinci was a full professor in the Electrical Engineering Department at Istanbul Technical University (ITU) from 2020 until February 2024. He also served as the Vice Dean of the Graduate School and the Electrical and Electronics Engineering Faculty from 2020 to 2021. Between 2021 and 2024, Dr. Akinci was a visiting scholar at the University of California Riverside (UCR). Currently he is an Assistant Project Scientist at the Bourns College of Engineering Center for Environmental Research and Technology (CE-CERT), UCR. His research interests include artificial neural networks, deep learning, machine learning, cognitive systems, signal processing, power systems, renewable energy systems, and data analysis. Alfredo A. Martinez-Morales serves as the Managing Director of the Southern California Research Initiative for Solar Energy (SC-RISE) and holds the position of Research Professor at the Bourns College of Engineering Center for Environmental Research and Technology (CE- CERT). He earned BS, MS, and PhD degrees in electrical engineering from the University of California Riverside (UCR) in 2005, 2008, and 2010, respectively. His current research focuses on solar cells, alkali metal ion batteries, highly integrated renewables, energy storage systems, and microgrids. Dr. Martinez-Morales plays a key role as a principal investigator in the Sustainable Integrated Grid Initiative (SIGI) and the Distributed Energy Resources Laboratory (DERL) at UCR, contributing to the engineering, permitting, and deployment of microgrids throughout Southern California.
1. Introduction. 2. Artificial Neuron and Its Mathematical Model. 3. Information Content of Neural Network Topology. 4. Back Propagation Algorithm. 5. Dynamical Neural Networks. 6. System Identification and Use of Neural Networks. 7. Neural Network for Applications of Control Theory. 8. Condition Monitoring with Neural Nets.
| Erscheint lt. Verlag | 23.4.2026 |
|---|---|
| Zusatzinfo | 6 Tables, black and white; 90 Line drawings, black and white; 90 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| ISBN-10 | 1-041-04911-0 / 1041049110 |
| ISBN-13 | 978-1-041-04911-1 / 9781041049111 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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