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Identification And Control Of Dynamical Systems Using Neural Networks
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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 Nonlinear Systems Using Neural Networks and Polynomial Models by : Andrzej Janczak
Download or read book Identification of Nonlinear Systems Using Neural Networks and Polynomial Models written by Andrzej Janczak and published by Springer Science & Business Media. This book was released on 2004-11-18 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.
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 MATLAB®.
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 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.
Book Synopsis Learning Automata by : Kumpati S. Narendra
Download or read book Learning Automata written by Kumpati S. Narendra and published by Courier Corporation. This book was released on 2013-05-27 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This self-contained introductory text on the behavior of learning automata focuses on how a sequential decision-maker with a finite number of choices responds in a random environment. Topics include fixed structure automata, variable structure stochastic automata, convergence, 0 and S models, nonstationary environments, interconnected automata and games, and applications of learning automata. A must for all students of stochastic algorithms, this treatment is the work of two well-known scientists and is suitable for a one-semester graduate course in automata theory and stochastic algorithms. This volume also provides a fine guide for independent study and a reference for students and professionals in operations research, computer science, artificial intelligence, and robotics. The authors have provided a new preface for this edition.
Book Synopsis System Identification and Adaptive Control by : Yiannis Boutalis
Download or read book System Identification and Adaptive Control written by Yiannis Boutalis and published by Springer Science & Business. This book was released on 2014-04-23 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in: • contemporary power generation; • process control and • conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.
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 2001-09-24 with total page 234 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 Neural Network Control of Nonlinear Discrete-Time Systems by : Jagannathan Sarangapani
Download or read book Neural Network Control of Nonlinear Discrete-Time Systems written by Jagannathan Sarangapani and published by CRC Press. This book was released on 2018-10-03 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.
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 Neural Network Systems Techniques and Applications by :
Download or read book Neural Network Systems Techniques and Applications written by and published by Academic Press. This book was released on 1998-02-09 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. 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
Book Synopsis Computational Intelligence in Systems and Control Design and Applications by : S.G. Tzafestas
Download or read book Computational Intelligence in Systems and Control Design and Applications written by S.G. Tzafestas and published by Springer Science & Business Media. This book was released on 2001-11-30 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains thirty timely contributions in the emerging field of Computational Intelligence (CI) with reference to system control design and applications. The three basic constituents ofCI are neural networks (NNs). fuzzy logic (FL) I fuzzy reasoning (FR). and genetic algorithms (GAs). NNs mimic the distributed functioning of the human brain and consist of many. rather simple. building elements (called artificial neurons) which are controlled by adaptive parameters and are able to incorporate via learning the knowledge provided by the environment, and thus respond intelligently to new stimuli. Fuzzy logic (FL) provides the means to build systems that can reason linguistically under uncertainty like the human experts (common sense reasoning). Both NNs and FL I FR are among the most widely used tools for modeling unknown systems with nonlinear behavior. FL suits better when there is some kind of knowledge about the system. such as, for example, the linguistic information of a human expert. On the other hand. NNs possess unique learning and generalization capabilities that allow the user to construct very accurate models of nonlinear systems simply using input-output data. GAs offer an interesting set of generic tools for systematic random search optimization following the mechanisms of natural genetics. In hybrid Computational Intelligence - based systems these three tools (NNs, FL, GAs) are combined in several synergetic ways producing integrated tools with enhanced learning, generalization. universal approximation. reasoning and optimization abilities.
Book Synopsis Nonlinear Dynamical Systems by : Irwin W. Sandberg
Download or read book Nonlinear Dynamical Systems written by Irwin W. Sandberg and published by John Wiley & Sons. This book was released on 2001-02-21 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sechs erfahrene Autoren beschreiben in diesem Band ein Spezialgebiet der neuronalen Netze mit Anwendungen in der Signalsteuerung, Signalverarbeitung und Zeitreihenanalyse. Ein zeitgemäßer Beitrag zur Behandlung nichtlinear-dynamischer Systeme!
Book Synopsis Intelligent Control Systems Using Computational Intelligence Techniques by : A.E. Ruano
Download or read book Intelligent Control Systems Using Computational Intelligence Techniques written by A.E. Ruano and published by IET. This book was released on 2005-07-18 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Control techniques are becoming important tools in both academia and industry. Methodologies developed in the field of soft-computing, such as neural networks, fuzzy systems and evolutionary computation, can lead to accommodation of more complex processes, improved performance and considerable time savings and cost reductions. Intelligent Control Systems using Computational Intellingence Techniques details the application of these tools to the field of control systems. Each chapter gives and overview of current approaches in the topic covered, with a set of the most important references in the field, and then details the author's approach, examining both the theory and practical applications.
Book Synopsis Differential Neural Networks for Robust Nonlinear Control by : Alexander S. Poznyak
Download or read book Differential Neural Networks for Robust Nonlinear Control written by Alexander S. Poznyak and published by World Scientific. This book was released on 2001 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.).
Book Synopsis Modelling and Control of Dynamic Systems Using Gaussian Process Models by : Juš Kocijan
Download or read book Modelling and Control of Dynamic Systems Using Gaussian Process Models written by Juš Kocijan and published by Springer. This book was released on 2015-11-21 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.
Book Synopsis Advances in Neural Networks - ISNN 2007 by : Derong Liu
Download or read book Advances in Neural Networks - ISNN 2007 written by Derong Liu and published by Springer. This book was released on 2007-07-14 with total page 1390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.
