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Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Artificial Neural Networks for Modelling and Control of Non-Linear Systems
A Book

by Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor

  • Publisher : Springer Science & Business Media
  • Release : 2012-12-06
  • Pages : 235
  • ISBN : 1475724934
  • Language : En, Es, Fr & De
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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 Modelling and Control

Neural Networks Modelling and Control
Applications for Unknown Nonlinear Delayed Systems in Discrete-Time

by Alma Y. Alanis,Jorge D. Rios,Carlos Lopez-Franco,Nancy Arana-Daniel,Edgar N. Sanchez

  • Publisher : Academic Press
  • Release : 2019-10
  • Pages : 158
  • ISBN : 9780128170786
  • Language : En, Es, Fr & De
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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.

Neural Networks Modeling and Control

Neural Networks Modeling and Control
Applications for Unknown Nonlinear Delayed Systems in Discrete Time

by Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco

  • Publisher : Academic Press
  • Release : 2020-01-15
  • Pages : 158
  • ISBN : 0128170794
  • Language : En, Es, Fr & De
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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

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

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

by Ömer Sinan Genç,İzmir Yüksek Teknoloji Enstitüsü

  • Publisher : Unknown Publisher
  • Release : 2006
  • Pages : 186
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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.

Neural Network Modeling and Control of a Gyro Mirror System

Neural Network Modeling and Control of a Gyro Mirror System
A Book

by Ching Ping Wong

  • Publisher : Unknown Publisher
  • Release : 1999
  • Pages : 240
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Neural Network Models

Neural Network Models
Theory and Projects

by Philippe de Wilde

  • Publisher : Springer Science & Business Media
  • Release : 1997-05-30
  • Pages : 174
  • ISBN : 9783540761297
  • Language : En, Es, Fr & De
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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.

Neural Networks for Control

Neural Networks for Control
A Book

by W. Thomas Miller,Richard S. Sutton,Paul J. Werbos

  • Publisher : MIT Press
  • Release : 1995
  • Pages : 544
  • ISBN : 9780262631617
  • Language : En, Es, Fr & De
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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

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

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

by Krzysztof Patan

  • Publisher : Springer Science & Business Media
  • Release : 2008-06-24
  • Pages : 206
  • ISBN : 3540798714
  • Language : En, Es, Fr & De
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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.

Neural Network Modeling and Control

Neural Network Modeling and Control
A Book

by Zhengwei Wu

  • Publisher : Unknown Publisher
  • Release : 1999
  • Pages : 160
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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A Comprehensive Guide to Neural Network Modeling

A Comprehensive Guide to Neural Network Modeling
A Book

by Steffen Skaar

  • Publisher : Nova Science Publishers
  • Release : 2020-10-26
  • Pages : 172
  • ISBN : 9781536185423
  • Language : En, Es, Fr & De
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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 Control

Neural Network Modeling and Control
Case Studies in Chemical Engineering

by Bernhard Eikens

  • Publisher : Unknown Publisher
  • Release : 1996
  • Pages : 528
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Neural Network Modeling and Control of a Flow Tank

Neural Network Modeling and Control of a Flow Tank
A Book

by Aaron W. Hart

  • Publisher : Unknown Publisher
  • Release : 1995
  • Pages : 90
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Artificial Neural Networks in Food Processing

Artificial Neural Networks in Food Processing
Modeling and Predictive Control

by Mohamed Tarek Khadir

  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 2021-01-18
  • Pages : 200
  • ISBN : 3110646056
  • Language : En, Es, Fr & De
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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 Networks for Cooperative Control of Multiple Robot Arms

Neural Networks for Cooperative Control of Multiple Robot Arms
A Book

by Shuai Li,Yinyan Zhang

  • Publisher : Springer
  • Release : 2017-10-29
  • Pages : 74
  • ISBN : 9811070377
  • Language : En, Es, Fr & De
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This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.

Application of Neural Networks to Modelling and Control

Application of Neural Networks to Modelling and Control
A Book

by G. F. Page,J. B. Gomm,D. Williams

  • Publisher : Unknown Publisher
  • Release : 1993
  • Pages : 119
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Knowledge-based Artificial Neural Network for Process Modelling and Control

Knowledge-based Artificial Neural Network for Process Modelling and Control
A Book

by Gary M. Scott

  • Publisher : Unknown Publisher
  • Release : 1993
  • Pages : 704
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems
A Book

by Yury Tiumentsev,Mikhail Egorchev

  • Publisher : Academic Press
  • Release : 2019-05-17
  • Pages : 332
  • ISBN : 0128154306
  • Language : En, Es, Fr & De
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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

Network Models for Control and Processing

Network Models for Control and Processing
A Book

by Martin D. Fraser

  • Publisher : Intellect Books
  • Release : 2000
  • Pages : 198
  • ISBN : 9781841500065
  • Language : En, Es, Fr & De
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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.

DNA Computing Based Genetic Algorithm

DNA Computing Based Genetic Algorithm
Applications in Industrial Process Modeling and Control

by Jili Tao,Ridong Zhang,Yong Zhu

  • Publisher : Springer Nature
  • Release : 2020-07-01
  • Pages : 274
  • ISBN : 981155403X
  • Language : En, Es, Fr & De
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This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities.

Identification of Nonlinear Systems Using Neural Networks and Polynomial Models

Identification of Nonlinear Systems Using Neural Networks and Polynomial Models
A Block-Oriented Approach

by Andrzej Janczak

  • Publisher : Springer Science & Business Media
  • Release : 2004-11-18
  • Pages : 199
  • ISBN : 9783540231851
  • Language : En, Es, Fr & De
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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.