[PDF] On Neural Networks In Identification And Control Of Dynamic Systems eBook

Author : National Aeronautics and Space Administration (NASA)
Publisher : Createspace Independent Publishing Platform
Page : 34 pages
File Size : 31,2 MB
Release : 2018-07-09
Category :
ISBN : 9781722451714

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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...

Author : Duc T. Pham
Publisher : Springer Science & Business Media
Page : 243 pages
File Size : 11,70 MB
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 1447132440

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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 : Yuri Tiumentsev
Publisher : Academic Press
Page : 332 pages
File Size : 11,77 MB
Release : 2019-05-17
Category : Science
ISBN : 0128154306

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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

Author : Cornelius T. Leondes
Publisher :
Page : 438 pages
File Size : 10,29 MB
Release : 1998
Category : Computers
ISBN : 0124438679

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The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies. Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multi-layer recurrent neural networks for synthesizing and implementing real-time linear control,adaptive control of unknown nonlinear dynamical systems, Optimal Tracking Neural Controller techniques, a consideration of unified approximation theory and applications, techniques for the determination of multi-variable nonlinear model structures for dynamic systems with a detailed treatment of relevant system model input determination, High Order Neural Networks and Recurrent High Order Neural Networks, High Order Moment Neural Array Systems, Online Learning Neural Network controllers, and Radial Bias Function techniques. Key Features Coverage includes: * Orthogonal Activation Function Based Neural Network System Architecture (OAFNN) * Multilayer recurrent neural networks for synthesizing and implementing real-time linear control * Adaptive control of unknown nonlinear dynamical systems * Optimal Tracking Neural Controller techniques * Consideration of unified approximation theory and applications * Techniques for determining multivariable nonlinear model structures for dynamic systems, with a detailed treatment of relevant system model input determination