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Identification And Control Of Dynamic System Using Neural Networks
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Book Synopsis Identification and Control of Dynamic Systems Using Neural Networks by : S. J. Oh
Download or read book Identification and Control of Dynamic Systems Using Neural Networks written by S. J. Oh and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On Neural Networks in Identification and Control of Dynamic Systems by : Minh Phan
Download or read book On Neural Networks in Identification and Control of Dynamic Systems written by Minh Phan and published by . This book was released on 1993 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification and Control of Dynamic System Using Neural Networks by : Sea-June Oh
Download or read book Identification and Control of Dynamic System Using Neural Networks written by Sea-June Oh and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification and Control of Dynamic Systems Using Neural Networks by : E. Colina Morles
Download or read book Identification and Control of Dynamic Systems Using Neural Networks written by E. Colina Morles and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neural Networks for Modelling and Control of Dynamic Systems by : M. Norgaard
Download or read book Neural Networks for Modelling and Control of Dynamic Systems written by M. Norgaard and published by . This book was released on 2003 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis IDENTIFICATION AND CONTROL OF DYNAMICAL SYSTEMS USING NEURAL NETWORKS. by : K. NARENDA
Download or read book IDENTIFICATION AND CONTROL OF DYNAMICAL SYSTEMS USING NEURAL NETWORKS. written by K. NARENDA and published by . This book was released on with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neural Systems for Control by : Omid Omidvar
Download or read book Neural Systems for Control written by Omid Omidvar and published by Elsevier. This book was released on 1997-02-24 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Identification of Dynamic Systems by : Rolf Isermann
Download or read book Identification of Dynamic Systems written by Rolf Isermann and published by Springer Science & Business Media. This book was released on 2010-11-22 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Networks for Identification, Prediction and Control by : Duc T. Pham
Download or read book Neural Networks for Identification, Prediction and Control written by Duc T. Pham and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described.
Author :National Aeronautics and Space Administration (NASA) Publisher :Createspace Independent Publishing Platform ISBN 13 :9781722451714 Total Pages :34 pages Book Rating :4.4/5 (517 download)
Book Synopsis On Neural Networks in Identification and Control of Dynamic Systems by : National Aeronautics and Space Administration (NASA)
Download or read book On Neural Networks in Identification and Control of Dynamic Systems written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-07-09 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts. Phan, Minh and Juang, Jer-Nan and Hyland, David C. Langley Research Center RTOP 585-03-11-09...
Book Synopsis Nonlinear Identification and Control by : G.P. Liu
Download or read book Nonlinear Identification and Control written by G.P. Liu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton
Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLABĀ®.
Book Synopsis Model-based Identification and Control of Nonlinear Dynamic Systems Using Neural Networks by : Ssu-Hsin Yu
Download or read book Model-based Identification and Control of Nonlinear Dynamic Systems Using Neural Networks written by Ssu-Hsin Yu and published by . This book was released on 1996 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification and Control of Dynamic Systems Via Adaptive Neural Networks by : E. Colina Morles
Download or read book Identification and Control of Dynamic Systems Via Adaptive Neural Networks written by E. Colina Morles and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification and Control of Non-linear Time-varying Dynamical Systems Using Artificial Neural Networks by : Shahar Dror
Download or read book Identification and Control of Non-linear Time-varying Dynamical Systems Using Artificial Neural Networks written by Shahar Dror and published by . This book was released on 1992 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Identification and Control of Non-linear Dynamic Systems Using Partially Recurrent Neural Networks by : Karl Gaffney
Download or read book The Identification and Control of Non-linear Dynamic Systems Using Partially Recurrent Neural Networks written by Karl Gaffney and published by . This book was released on 1995 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neural Network Modeling and Identification of Dynamical Systems by : Yuri Tiumentsev
Download or read book Neural Network Modeling and Identification of Dynamical Systems written by Yuri Tiumentsev and published by Academic Press. This book was released on 2019-05-17 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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