Neural Networks for Modelling and Control of Dynamic Systems
Title | Neural Networks for Modelling and Control of Dynamic Systems PDF eBook |
Author | M. Norgaard |
Publisher | |
Total Pages | 246 |
Release | 2003 |
Genre | |
ISBN |
Neural Networks For Modelling And Control Of Dynamic Systems: A Practitioner'S Handbook
Title | Neural Networks For Modelling And Control Of Dynamic Systems: A Practitioner'S Handbook PDF eBook |
Author | Norgaard |
Publisher | |
Total Pages | 260 |
Release | 2009-09-01 |
Genre | |
ISBN | 9788184893687 |
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Title | Artificial Neural Networks for Modelling and Control of Non-Linear Systems PDF eBook |
Author | Johan A.K. Suykens |
Publisher | Springer Science & Business Media |
Total Pages | 242 |
Release | 2012-12-06 |
Genre | Technology & Engineering |
ISBN | 1475724934 |
Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.
On Neural Networks in Identification and Control of Dynamic Systems
Title | On Neural Networks in Identification and Control of Dynamic Systems PDF eBook |
Author | Minh Phan |
Publisher | |
Total Pages | 38 |
Release | 1993 |
Genre | |
ISBN |
Neural Systems for Control
Title | Neural Systems for Control PDF eBook |
Author | Omid Omidvar |
Publisher | Elsevier |
Total Pages | 375 |
Release | 1997-02-24 |
Genre | Computers |
ISBN | 0080537391 |
Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory Represents the most up-to-date developments in this rapidly growing application area of neural networks Takes a new and novel approach to system identification and synthesis
Neural Network Modeling and Identification of Dynamical Systems
Title | Neural Network Modeling and Identification of Dynamical Systems PDF eBook |
Author | Yuri Tiumentsev |
Publisher | Academic Press |
Total Pages | 332 |
Release | 2019-05-17 |
Genre | Science |
ISBN | 0128154306 |
Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area
Neural Network Engineering in Dynamic Control Systems
Title | Neural Network Engineering in Dynamic Control Systems PDF eBook |
Author | Kenneth J. Hunt |
Publisher | Springer Science & Business Media |
Total Pages | 285 |
Release | 2012-12-06 |
Genre | Technology & Engineering |
ISBN | 1447130669 |
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, .... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Within the control community there has been much discussion of and interest in the new Emerging Technologies and Methods. Neural networks along with Fuzzy Logic and Expert Systems is an emerging methodology which has the potential to contribute to the development of intelligent control technologies. This volume of some thirteen chapters edited by Kenneth Hunt, George Irwin and Kevin Warwick makes a useful contribution to the literature of neural network methods and applications. The chapters are arranged systematically progressing from theoretical foundations, through the training aspects of neural nets and concluding with four chapters of applications. The applications include problems as diverse as oven tempera ture control, and energy/load forecasting routines. We hope this interesting but balanced mix of material appeals to a wide range of readers from the theoretician to the industrial applications engineer.