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 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 Networks for Control

Neural Networks for Control
Author: W. Thomas Miller,Richard S. Sutton,Paul J. Werbos
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 brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series

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: 1928374650XXX
ISBN 13: OCLC:876537456
Language: EN, FR, DE, ES & NL

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

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

Neural Network Modeling and Control

Neural Network Modeling and Control
Author: Zhengwei Wu
Publsiher: Unknown
Total Pages: 160
Release: 1999
ISBN 10: 1928374650XXX
ISBN 13: OCLC:41999506
Language: EN, FR, DE, ES & NL

Neural Network Modeling and Control Book Review:

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.

Artificial Neural Networks in Food Processing

Artificial Neural Networks in Food Processing
Author: Mohamed Tarek Khadir
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 200
Release: 2021-01-18
ISBN 10: 3110646056
ISBN 13: 9783110646054
Language: EN, FR, DE, ES & NL

Artificial Neural Networks in Food Processing Book Review:

Artificial Neural Networks (ANNs) is a powerful computational tool to mimic the learning process of the mammalian brain. This book gives a comprehensive overview of ANNs including an introduction to the topic, classifications of single neurons and neural networks, model predictive control and a review of ANNs used in food processing. Also, examples of ANNs in food processing applications such as pasteurization control are illustrated.

Neural Network Modeling and Control

Neural Network Modeling and Control
Author: Bernhard Eikens
Publsiher: Unknown
Total Pages: 528
Release: 1996
ISBN 10: 1928374650XXX
ISBN 13: OCLC:36912212
Language: EN, FR, DE, ES & NL

Neural Network Modeling and Control Book Review:

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

Network Models for Control and Processing

Network Models for Control and Processing
Author: Martin D. Fraser
Publsiher: Intellect Books
Total Pages: 198
Release: 2000
ISBN 10: 9781841500065
ISBN 13: 1841500062
Language: EN, FR, DE, ES & NL

Network Models for Control and Processing Book Review:

This book provides a powerful tool for collecting and correlating related bodies of research in modelling control and processing in distributed networks. While traditional publications in the field of network models have focussed on specific areas, this successfully intersects many related fields. These cover: control processes, modelling features and operations of biological neural networks and neurons, simulation of biological experimentation, and representation of artificial neural networks (ANNs) Within the fields mentioned, the topics discussed include: control solutions using theoretical computational learning models, learning algorithms and polynomial networks; simulating biological experimentation and physical mechanisms with computer-assisted and hardware models of biological neural networks and neurons; improving processes for representing artificial neural networks by verification from SPICE and global optimization techniques.

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

Gas Turbines Modeling Simulation and Control

Gas Turbines Modeling  Simulation  and Control
Author: Hamid Asgari,XiaoQi Chen
Publsiher: CRC Press
Total Pages: 206
Release: 2015-10-16
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 consi

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.

Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems
Author: Yuri Tiumentsev,Mikhail Egorchev
Publsiher: Academic Press
Total Pages: 332
Release: 2019-05-17
ISBN 10: 0128154306
ISBN 13: 9780128154304
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 Formal Models and Their Applications ICANN 2005

Artificial Neural Networks  Formal Models and Their Applications     ICANN 2005
Author: Wlodzislaw Duch,Janusz Kacprzyk,Erkki Oja,Slawomir Zadrozny
Publsiher: Springer Science & Business Media
Total Pages: 1045
Release: 2005-08-31
ISBN 10: 3540287558
ISBN 13: 9783540287551
Language: EN, FR, DE, ES & NL

Artificial Neural Networks Formal Models and Their Applications ICANN 2005 Book Review:

The two volume set LNCS 3696 and LNCS 3697 constitutes the refereed proceedings of the 15th International Conference on Artificial Neural Networks, ICANN 2005, held in Warsaw, Poland in September 2005. The over 600 papers submitted to ICANN 2005 were thoroughly reviewed and carefully selected for presentation. The first volume includes 106 contributions related to Biological Inspirations; topics addressed are modeling the brain and cognitive functions, development of cognitive powers in embodied systems spiking neural networks, associative memory models, models of biological functions, projects in the area of neuroIT, evolutionary and other biological inspirations, self-organizing maps and their applications, computer vision, face recognition and detection, sound and speech recognition, bioinformatics, biomedical applications, and information- theoretic concepts in biomedical data analysis. The second volume contains 162 contributions related to Formal Models and their Applications and deals with new neural network models, supervised learning algorithms, ensemble-based learning, unsupervised learning, recurent neural networks, reinforcement learning, bayesian approaches to learning, learning theory, artificial neural networks for system modeling, decision making, optimalization and control, knowledge extraction from neural networks, temporal data analysis, prediction and forecasting, support vector machines and kernel-based methods, soft computing methods for data representation, analysis and processing, data fusion for industrial, medical and environmental applications, non-linear predictive models for speech processing, intelligent multimedia and semantics, applications to natural language processing, various applications, computational intelligence in games, and issues in hardware implementation.

Neural Network Modeling and Control of a Gyro Mirror System

Neural Network Modeling and Control of a Gyro Mirror System
Author: Ching Ping Wong
Publsiher: Unknown
Total Pages: 240
Release: 1999
ISBN 10: 1928374650XXX
ISBN 13: OCLC:969906629
Language: EN, FR, DE, ES & NL

Neural Network Modeling and Control of a Gyro Mirror System Book Review:

Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes

Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes
Author: Krzysztof Patan
Publsiher: Springer Science & Business Media
Total Pages: 206
Release: 2008-06-24
ISBN 10: 3540798714
ISBN 13: 9783540798712
Language: EN, FR, DE, ES & NL

Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes Book Review:

An unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault diagnosis strategies. This is especially true for engineering systems,whose complexity is permanently growing due to the inevitable development of modern industry as well as the information and communication technology revolution. Indeed, the design and operation of engineering systems require an increased attention with respect to availability, reliability, safety and fault tolerance. Thus, it is natural that fault diagnosis plays a fundamental role in modern control theory and practice. This is re?ected in plenty of papers on fault diagnosis in many control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon model basedfault diagnosis has been accumulated through scienti?c literature since the beginning of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available.

Modelling and Control of Bioprocesses by Using Artificial Neural Networks and Hybridmodel

Modelling and Control of Bioprocesses by Using Artificial Neural Networks and Hybridmodel
Author: Ömer Sinan Genç,İzmir Yüksek Teknoloji Enstitüsü
Publsiher: Unknown
Total Pages: 186
Release: 2006
ISBN 10: 1928374650XXX
ISBN 13: OCLC:416390761
Language: EN, FR, DE, ES & NL

Modelling and Control of Bioprocesses by Using Artificial Neural Networks and Hybridmodel Book Review:

The aim of this study is modeling and control of bioprocesses by using neural networks and hybrid model techniques. To investigate the modeling techniques, ethanol fermentation with Saccharomyces Cerevisiae and recombinant Zymomonas mobilis and finally gluconic acid fermentation with Pseudomonas ovalis processes are chosen.Model equations of these applications are obtained from literature. Numeric solutions are done in Matlab by using ODE solver. For neural network modeling a part of the numerical data is used for training of the network.In hybrid modeling technique, model equations which are obtained from literature are first linearized then to constitute the hybrid model linearized solution results are subtracted from numerical results and obtained values are taken as nonlinear part of the process. This nonlinear part is then solved by neural networks and the results of the neural networks are summed with the linearized solution results. This summation results constitute the hybrid model of the process. Hybrid and neural network models are compared. In some of the applications hybrid model gives slightly better results than the neural network model. But in all of the applications, required training time is much more less for hybrid model techniques. Also, it is observed that hybrid model obeys the physical constraints but neural network model solutions sometimes give meaningless outputs.In control application, a method is demonstrated for optimization of a bioprocess by using hybrid model with neural network structure. To demonstrate the optimization technique, a well known fermentation process is chosen from the literature.

Smart Computing

Smart Computing
Author: Mohammad Ayoub Khan,Sanjay Gairola,Bhola Jha,Pushkar Praveen
Publsiher: CRC Press
Total Pages: 1000
Release: 2021-05-12
ISBN 10: 1000382613
ISBN 13: 9781000382617
Language: EN, FR, DE, ES & NL

Smart Computing Book Review:

The field of SMART technologies is an interdependent discipline. It involves the latest burning issues ranging from machine learning, cloud computing, optimisations, modelling techniques, Internet of Things, data analytics, and Smart Grids among others, that are all new fields. It is an applied and multi-disciplinary subject with a focus on Specific, Measurable, Achievable, Realistic & Timely system operations combined with Machine intelligence & Real-Time computing. It is not possible for any one person to comprehensively cover all aspects relevant to SMART Computing in a limited-extent work. Therefore, these conference proceedings address various issues through the deliberations by distinguished Professors and researchers. The SMARTCOM 2020 proceedings contain tracks dedicated to different areas of smart technologies such as Smart System and Future Internet, Machine Intelligence and Data Science, Real-Time and VLSI Systems, Communication and Automation Systems. The proceedings can be used as an advanced reference for research and for courses in smart technologies taught at graduate level.