Neural Networks for Modelling and Control of Dynamic Systems

Neural Networks for Modelling and Control of Dynamic Systems
Author: M. Norgaard
Publsiher: Unknown
Total Pages: 246
Release: 2003
ISBN 10:
ISBN 13: OCLC:876537456
Language: EN, FR, DE, ES & NL

Neural Networks for Modelling and Control of Dynamic Systems Book Review:

Neural Networks for Control

Neural Networks for Control
Author: W. Thomas Miller,Paul J. Werbos,Richard S. Sutton
Publsiher: MIT Press
Total Pages: 544
Release: 1995
ISBN 10: 9780262631617
ISBN 13: 026263161X
Language: EN, FR, DE, ES & NL

Neural Networks for Control Book Review:

Neural Networks for Control highlights key issues in learning control and identifiesresearch directions that could lead to practical solutions for control problems in criticalapplication domains. It addresses general issues of neural network based control and neural networklearning with regard to specific problems of motion planning and control in robotics, and takes upapplication domains well suited to the capabilities of neural network controllers. The appendixdescribes seven benchmark control problems.W. Thomas Miller, III is Professor of Electrical andComputer Engineering at the University of New Hampshire. Richard S. Sutton works for GTELaboratories Incorporated. Paul J. Werbos is Program Director for Neuroengineering at the NationalScience Foundation.Contributors: Andrew G. Barto. Ronald J. Williams. Paul J. Werbos. Kumpati S.Narendra. L. Gordon Kraft, III, David P. Campagna. Mitsuo Kawato. Bartlett W. Met. Christopher G.Atkeson, David J. Reinkensmeyer. Derrick Nguyen, Bernard Widrow. James C. Houk, Satinder P. Singh,Charles Fisher. Judy A. Franklin, Oliver G. Selfridge. Arthur C. Sanderson. Lyle H. Ungar. CharlesC. Jorgensen, C. Schley. Martin Herman, James S. Albus, Tsai-Hong Hong. Charles W. Anderson, W.Thomas Miller, III.

Artificial Neural Networks for Modelling and Control of Non Linear Systems

Artificial Neural Networks for Modelling and Control of Non Linear Systems
Author: Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor
Publsiher: Springer Science & Business Media
Total Pages: 235
Release: 2012-12-06
ISBN 10: 1475724934
ISBN 13: 9781475724936
Language: EN, FR, DE, ES & NL

Artificial Neural Networks for Modelling and Control of Non Linear Systems Book Review:

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Neural Systems for Control

Neural Systems for Control
Author: Omid Omidvar,David L. Elliott
Publsiher: Elsevier
Total Pages: 358
Release: 1997-02-24
ISBN 10: 9780080537399
ISBN 13: 0080537391
Language: EN, FR, DE, ES & NL

Neural Systems for Control Book Review:

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

Artificial Neural Networks for Modelling and Control of Non Linear Systems

Artificial Neural Networks for Modelling and Control of Non Linear Systems
Author: Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor
Publsiher: Springer Science & Business Media
Total Pages: 235
Release: 2012-12-06
ISBN 10: 1475724934
ISBN 13: 9781475724936
Language: EN, FR, DE, ES & NL

Artificial Neural Networks for Modelling and Control of Non Linear Systems Book Review:

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Neural Networks for Identification Prediction and Control

Neural Networks for Identification  Prediction and Control
Author: Duc T. Pham,Xing Liu
Publsiher: Springer Science & Business Media
Total Pages: 238
Release: 2012-12-06
ISBN 10: 1447132440
ISBN 13: 9781447132448
Language: EN, FR, DE, ES & NL

Neural Networks for Identification Prediction and Control Book Review:

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.

Gas Turbines Modeling Simulation and Control

Gas Turbines Modeling  Simulation  and Control
Author: Hamid Asgari,XiaoQi Chen
Publsiher: CRC Press
Total Pages: 176
Release: 2016-02-12
ISBN 10: 1498777546
ISBN 13: 9781498777544
Language: EN, FR, DE, ES & NL

Gas Turbines Modeling Simulation and Control Book Review:

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book: Outlines important criteria to consider at the beginning of the GT modeling process, such as GT types and configurations, control system types and configurations, and modeling methods and objectives Highlights research in the fields of white-box and black-box modeling, simulation, and control of GTs, exploring models of low-power GTs, industrial power plant gas turbines (IPGTs), and aero GTs Discusses the structure of ANNs and the ANN-based model-building process, including system analysis, data acquisition and preparation, network architecture, and network training and validation Presents a noteworthy ANN-based methodology for offline system identification of GTs, complete with validated models using both simulated and real operational data Covers the modeling of GT transient behavior and start-up operation, and the design of proportional-integral-derivative (PID) and neural network-based controllers Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks not only offers a comprehensive review of the state of the art of gas turbine modeling and intelligent techniques, but also demonstrates how artificial intelligence can be used to solve complicated industrial problems, specifically in the area of GTs.

Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems
Author: Yury Tiumentsev,Mikhail Egorchev
Publsiher: Academic Press
Total Pages: 332
Release: 2019-05-17
ISBN 10: 9780128152546
ISBN 13: 0128152540
Language: EN, FR, DE, ES & NL

Neural Network Modeling and Identification of Dynamical Systems Book Review:

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

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
Author: Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publsiher: Academic Press
Total Pages: 224
Release: 2019-03-15
ISBN 10: 0128182474
ISBN 13: 9780128182475
Language: EN, FR, DE, ES & NL

Artificial Neural Networks for Engineering Applications Book Review:

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the book contains all the theory required to use the proposed methodologies for different applications. Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering Contains all the theory required to use the proposed methodologies for different applications

Neural Network Engineering in Dynamic Control Systems

Neural Network Engineering in Dynamic Control Systems
Author: Kenneth J. Hunt,George R. Irwin,Kevin Warwick
Publsiher: Springer Science & Business Media
Total Pages: 282
Release: 2012-12-06
ISBN 10: 1447130669
ISBN 13: 9781447130666
Language: EN, FR, DE, ES & NL

Neural Network Engineering in Dynamic Control Systems Book Review:

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts 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. Within the control community there has been much discussion of and interest in the new Emerging Technologies and Methods. Neural networks along with Fuzzy Logic and Expert Systems is an emerging methodology which has the potential to contribute to the development of intelligent control technologies. This volume of some thirteen chapters edited by Kenneth Hunt, George Irwin and Kevin Warwick makes a useful contribution to the literature of neural network methods and applications. The chapters are arranged systematically progressing from theoretical foundations, through the training aspects of neural nets and concluding with four chapters of applications. The applications include problems as diverse as oven tempera ture control, and energy/load forecasting routines. We hope this interesting but balanced mix of material appeals to a wide range of readers from the theoretician to the industrial applications engineer.

Neural Networks Modeling and Control

Neural Networks Modeling and Control
Author: Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publsiher: Academic Press
Total Pages: 158
Release: 2020-01-15
ISBN 10: 0128170794
ISBN 13: 9780128170793
Language: EN, FR, DE, ES & NL

Neural Networks Modeling and Control Book Review:

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control. As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends. Provide in-depth analysis of neural control models and methodologies Presents a comprehensive review of common problems in real-life neural network systems Includes an analysis of potential applications, prototypes and future trends

Neural Network Models

Neural Network Models
Author: Philippe de Wilde
Publsiher: Springer Science & Business Media
Total Pages: 174
Release: 1997-05-30
ISBN 10: 9783540761297
ISBN 13: 3540761292
Language: EN, FR, DE, ES & NL

Neural Network Models Book Review:

Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. It presents the essential principles of nonlinear dynamics as derived from neurobiology, and investigates the stability, convergence behaviour and capacity of networks.

Fuzzy Neural Networks for Real Time Control Applications

Fuzzy Neural Networks for Real Time Control Applications
Author: Erdal Kayacan,Mojtaba Ahmadieh Khanesar
Publsiher: Butterworth-Heinemann
Total Pages: 264
Release: 2015-10-07
ISBN 10: 0128027037
ISBN 13: 9780128027035
Language: EN, FR, DE, ES & NL

Fuzzy Neural Networks for Real Time Control Applications Book Review:

AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book! Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book. A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis. You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are: • Gradient descent • Levenberg-Marquardt • Extended Kalman filter In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced. The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully. Parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis Contains algorithms that are applicable to real time systems Introduces fast and simple adaptation rules for type-1 and type-2 fuzzy neural networks Number of case studies both in identification and control Provides MATLAB® codes for some algorithms in the book

A Comprehensive Guide to Neural Network Modeling

A Comprehensive Guide to Neural Network Modeling
Author: Steffen Skaar
Publsiher: Nova Science Publishers
Total Pages: 172
Release: 2020-10-26
ISBN 10: 9781536185423
ISBN 13: 1536185426
Language: EN, FR, DE, ES & NL

A Comprehensive Guide to Neural Network Modeling Book Review:

As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing on control, monitoring and modeling of industrial food processes.The authors emphasize the main achievements of artificial neural network modeling in recent years in the field of quantitative structure-activity relationships and quantitative structure-retention relationships.In the closing study, artificial intelligence techniques are applied to river water quality data and artificial intelligence models are developed in an effort to contribute to the reduction of the cost of future on-line measurement stations.

Artificial Higher Order Neural Networks for Modeling and Simulation

Artificial Higher Order Neural Networks for Modeling and Simulation
Author: Zhang, Ming
Publsiher: IGI Global
Total Pages: 454
Release: 2012-10-31
ISBN 10: 1466621761
ISBN 13: 9781466621763
Language: EN, FR, DE, ES & NL

Artificial Higher Order Neural Networks for Modeling and Simulation Book Review:

"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.

Neural Network Applications in Control

Neural Network Applications in Control
Author: Institution of Electrical Engineers
Publsiher: IET
Total Pages: 295
Release: 1995
ISBN 10: 9780852968529
ISBN 13: 0852968523
Language: EN, FR, DE, ES & NL

Neural Network Applications in Control Book Review:

Introducing a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks as well as the more useful multilayer perception back-propagation networks, this book aims to give a detailed appreciation of the use of neural nets in these applications.

Neural Network Modeling

Neural Network Modeling
Author: P. S. Neelakanta,Dolores DeGroff
Publsiher: CRC Press
Total Pages: 256
Release: 2018-02-06
ISBN 10: 1351428950
ISBN 13: 9781351428958
Language: EN, FR, DE, ES & NL

Neural Network Modeling Book Review:

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Artificial Neural Networks

Artificial Neural Networks
Author: Joao Luis Garcia Rosa
Publsiher: BoD – Books on Demand
Total Pages: 414
Release: 2016-10-19
ISBN 10: 9535127047
ISBN 13: 9789535127048
Language: EN, FR, DE, ES & NL

Artificial Neural Networks Book Review:

The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.

Process Modeling and Control of Enhanced Coagulation

Process Modeling and Control of Enhanced Coagulation
Author: Stephen John Stanley
Publsiher: American Water Works Association
Total Pages: 145
Release: 2000-01-01
ISBN 10: 1583210504
ISBN 13: 9781583210505
Language: EN, FR, DE, ES & NL

Process Modeling and Control of Enhanced Coagulation Book Review:

Neural Network Control of Nonlinear Discrete Time Systems

Neural Network Control of Nonlinear Discrete Time Systems
Author: Jagannathan Sarangapani
Publsiher: CRC Press
Total Pages: 624
Release: 2018-10-03
ISBN 10: 1420015451
ISBN 13: 9781420015454
Language: EN, FR, DE, ES & NL

Neural Network Control of Nonlinear Discrete Time Systems Book Review:

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.