Identification And Control Of Dynamical Systems Using Neural Networks PDF Download
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Author | : K. NARENDA |
Publisher | : |
Total Pages | : 0 |
Release | : |
Genre | : |
ISBN | : |
Download IDENTIFICATION AND CONTROL OF DYNAMICAL SYSTEMS USING NEURAL NETWORKS. Book in PDF, ePub and Kindle
Author | : Shahar Dror |
Publisher | : |
Total Pages | : 258 |
Release | : 1992 |
Genre | : Adaptive control systems |
ISBN | : |
Download Identification and Control of Non-linear Time-varying Dynamical Systems Using Artificial Neural Networks Book in PDF, ePub and Kindle
Author | : Rohini Gupta |
Publisher | : |
Total Pages | : 200 |
Release | : 1992 |
Genre | : |
ISBN | : |
Download Recurrent neural networks for identification and control of nonlinear dynamical systems Book in PDF, ePub and Kindle
Author | : Omid Omidvar |
Publisher | : Elsevier |
Total Pages | : 375 |
Release | : 1997-02-24 |
Genre | : Computers |
ISBN | : 0080537391 |
Download Neural Systems for Control Book in PDF, ePub and Kindle
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
Author | : G.P. Liu |
Publisher | : Springer Science & Business Media |
Total Pages | : 224 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1447103459 |
Download Nonlinear Identification and Control Book in PDF, ePub and Kindle
The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It uses a number of simulated and industrial examples throughout, to demonstrate the operation of nonlinear identification and control techniques using neural networks.
Author | : Minh Phan |
Publisher | : |
Total Pages | : 38 |
Release | : 1993 |
Genre | : |
ISBN | : |
Download On Neural Networks in Identification and Control of Dynamic Systems Book in PDF, ePub and Kindle
Author | : Hussain Naser Al-Duwaish |
Publisher | : |
Total Pages | : 428 |
Release | : 1995 |
Genre | : Nonlinear theories |
ISBN | : |
Download Identification and Control of Nonlinear Dynamical Systems Using Multilayer Feedforward Neural Networks and Autoregressive Moving Average Models Book in PDF, ePub and Kindle
Author | : Rolf Isermann |
Publisher | : Springer Science & Business Media |
Total Pages | : 705 |
Release | : 2010-11-22 |
Genre | : Technology & Engineering |
ISBN | : 3540788794 |
Download Identification of Dynamic Systems Book in PDF, ePub and Kindle
Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.
Author | : Shahar Dror |
Publisher | : |
Total Pages | : 0 |
Release | : 1992 |
Genre | : Adaptive control systems |
ISBN | : |
Download Identification and Control of Non-linear Time-varying Dynamical Systems Using Artificial Neural Networks Book in PDF, ePub and Kindle
Author | : Yuri Tiumentsev |
Publisher | : Academic Press |
Total Pages | : 332 |
Release | : 2019-05-17 |
Genre | : Science |
ISBN | : 0128154306 |
Download Neural Network Modeling and Identification of Dynamical Systems Book in PDF, ePub and Kindle
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