Adaptive Control with Recurrent High-order Neural Networks

Adaptive Control with Recurrent High-order Neural Networks
Author: George A. Rovithakis
Publisher: Springer Science & Business Media
Total Pages: 203
Release: 2012-12-06
Genre: Computers
ISBN: 1447107853
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The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on 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. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.

System Identification and Adaptive Control

System Identification and Adaptive Control
Author: Yiannis Boutalis
Publisher: Springer Science & Business
Total Pages: 316
Release: 2014-04-23
Genre: Technology & Engineering
ISBN: 3319063642
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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.

Artificial Higher Order Neural Networks for Modeling and Simulation

Artificial Higher Order Neural Networks for Modeling and Simulation
Author: Zhang, Ming
Publisher: IGI Global
Total Pages: 455
Release: 2012-10-31
Genre: Computers
ISBN: 1466621761
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"This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.

Control and Dynamic Systems

Control and Dynamic Systems
Author: Cornelius T. Leondes
Publisher:
Total Pages: 438
Release: 1998
Genre: 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

Applications of Neural Adaptive Control Technology

Applications of Neural Adaptive Control Technology
Author: Jens Kalkkuhl
Publisher: World Scientific
Total Pages: 328
Release: 1997
Genre: Technology & Engineering
ISBN: 9789810231514
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This book presents the results of the second workshop on Neural Adaptive Control Technology, NACT II, held on September 9-10, 1996, in Berlin. The workshop was organised in connection with a three-year European-Union-funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland).The NACT project, which began on 1 April 1994, is a study of the fundamental properties of neural-network-based adaptive control systems. Where possible, links with traditional adaptive control systems are exploited. A major aim is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from within the Daimler-Benz group of companies.The aim of the workshop was to bring together selected invited specialists in the fields of adaptive control, nonlinear systems and neural networks. The first workshop (NACT I) took place in Glasgow in May 1995 and was mainly devoted to theoretical issues of neural adaptive control. Besides monitoring further development of theory, the NACT II workshop was focused on industrial applications and software tools. This context dictated the focus of the book and guided the editors in the choice of the papers and their subsequent reshaping into substantive book chapters. Thus, with the project having progressed into its applications stage, emphasis is put on the transfer of theory of neural adaptive engineering into industrial practice. The contributors are therefore both renowned academics and practitioners from major industrial users of neurocontrol.

Neural Network Systems Techniques and Applications

Neural Network Systems Techniques and Applications
Author:
Publisher: Academic Press
Total Pages: 459
Release: 1998-02-09
Genre: Computers
ISBN: 0080553907
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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. 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

Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control

Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control
Author: Chun-Fei Hsu
Publisher:
Total Pages:
Release: 2008
Genre:
ISBN: 9789537619084
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This paper develops a recurrent-neural-network-based adaptive control (RNNAC) system with structure adaptation algorithm, which is composed of a neural controller and a robust controller. In the neural controller design, a self-structuring recurrent neural network (SRNN) is utilized to mimic an ideal tracking controller. In the SRNN approximator, a dynamic generating and pruning mechanism of the neural stricture is developed to cope with the tradeoff between the approximation accuracy and computation load. The robust controller is designed to attenuate the effects of the approximation error on the tracking performance using L2 tracking technique. Finally, the developed RNNAC system is used to control a nonlinear chaotic dynamic system to demonstrate its effectiveness. Simulation results indicate that a small attenuation level can be achieved if the magnitude of weighting factor is chosen small.

Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications

Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications
Author: Zhang, Ming
Publisher: IGI Global
Total Pages: 660
Release: 2010-02-28
Genre: Computers
ISBN: 1615207120
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"This book introduces and explains Higher Order Neural Networks (HONNs) to people working in the fields of computer science and computer engineering, and how to use HONNS in these areas"--Provided by publisher.

Discrete-Time High Order Neural Control

Discrete-Time High Order Neural Control
Author: Edgar N. Sanchez
Publisher: Springer
Total Pages: 116
Release: 2008-06-24
Genre: Technology & Engineering
ISBN: 3540782893
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Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.

Rollover Control in Heavy Vehicles Via Recurrent High Order Neural Networks

Rollover Control in Heavy Vehicles Via Recurrent High Order Neural Networks
Author: Luis J. Ricalde
Publisher:
Total Pages:
Release: 2008
Genre:
ISBN: 9789537619084
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In this paper an adaptive recurrent neural network controller is developed in order to prevent rollover in heavy vehicles. The control scheme is composed of an Recurrent Neural.