Product Description

A complete guide to the design and implementation of successful neurocontrol applications

Neurocontrol: Towards an Industrial Control Methodology is the first and only volume that presents a unified framework for neural network-based techniques. It demystifies neurocontroller design and promotes the broad application of neurocontrol to nonlinear control problems. Divided into two major parts —the theoretical and the practical —this book links neurocontrol with the concepts of classical control theory, describes the steps necessary to implement a working algorithm, and provides the information necessary to develop competitive applications of industrial size and complexity. Throughout, the focus is on the most important issues faced by control systems engineers working in this area, including

• Fundamental approaches to neurocontrol viewed as optimization tasks
• Neural network architectures for neurocontrol
• Learning algorithms viewed as optimization algorithms
• Identification of plant models from measured data
• Training of an optimal neurocontroller
• Robustness, adaptiveness, stability, and other special topics
• Implementation of neurocontrol applications

Supplemented with case studies of real-world industrial control applications —from car drive train control to wastewater treatment plant control —Neurocontrol is an important professional reference for control engineers in a wide range of industries as well as for automatic control and adaptive control researchers. It is also an excellent text for graduate and senior undergraduate students in neurocontrol and automatic control.

Product Details

• Amazon Sales Rank: #185200 in eBooks
• Published on: 1997-09-08
• Format: Kindle Book
• Number of items: 1

From the Publisher
Neurocontrol has wide applications in control system engineering, in areas such as anti-skid brakes control, satellite attitude control, active suspension control. This book provides a methodology for controller design and implementation with neural network based techniques, and links neurocontrol with classical control theory. Divided into two parts, the book starts with the basics of classical control and covers theory and concepts. It then presents case studies, including elastomer test bench control, car drive train control, and wastewater treatment plant control, to show the scope and complexity of applications that have been solved with help of a single neurocontrol method.

From the Back Cover
"A complete guide to the design and implementation of successful neurocontrol applications

Neurocontrol: Towards an Industrial Control Methodology is the first and only volume that presents a unified framework for neural network-based techniques. It demystifies neurocontroller design and promotes the broad application of neurocontrol to nonlinear control problems. Divided into two major parts —the theoretical and the practical —this book links neurocontrol with the concepts of classical control theory, describes the steps necessary to implement a working algorithm, and provides the information necessary to develop competitive applications of industrial size and complexity. Throughout, the focus is on the most important issues faced by control systems engineers working in this area, including Fundamental approaches to neurocontrol viewed as optimization tasks Neural network architectures for neurocontrol Learning algorithms viewed as optimization algorithms Identification of plant models from measured data Training of an optimal neurocontroller Robustness, adaptiveness, stability, and other special topics Implementation of neurocontrol applications

Supplemented with case studies of real-world industrial control applications —from car drive train control to wastewater treatment plant control —Neurocontrol is an important professional reference for control engineers in a wide range of industries as well as for automatic control and adaptive control researchers. It is also an excellent text for graduate and senior undergraduate students in neurocontrol and automatic control.

About the Author
Tomas Hrycej is Senior Researcher at the Daimler-Benz Research Center in Ulm, Germany; former senior researcher at PCS Computer Systems in Munich; and the author of Modular Learning in Neural Networks. The case studies presented in this book are based on Dr. Hrycej's work at the Daimler-Benz Research Center."