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Neural Networks Modeling And Control

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Neural Networks Modelling and Control

Neural Networks Modelling and Control Book
Author : Alma Y. Alanis,Jorge D. Rios,Carlos Lopez-Franco,Nancy Arana-Daniel,Edgar N. Sanchez
Publisher : Academic Press
Release : 2019-10
ISBN : 9780128170786
Language : En, Es, Fr & De

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Book Description :

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.

Artificial Neural Networks for Modelling and Control of Non Linear Systems

Artificial Neural Networks for Modelling and Control of Non Linear Systems Book
Author : Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor
Publisher : Springer Science & Business Media
Release : 2012-12-06
ISBN : 1475724934
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publisher : Academic Press
Release : 2020-01-15
ISBN : 0128170794
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Ömer Sinan Genç,İzmir Yüksek Teknoloji Enstitüsü
Publisher : Unknown
Release : 2006
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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 Networks for Control

Neural Networks for Control Book
Author : W. Thomas Miller,Paul J. Werbos,Richard S. Sutton
Publisher : MIT Press
Release : 1995
ISBN : 9780262631617
Language : En, Es, Fr & De

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Book Description :

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.

Neural Network Models

Neural Network Models Book
Author : Philippe de Wilde
Publisher : Springer Science & Business Media
Release : 1997-05-30
ISBN : 9783540761297
Language : En, Es, Fr & De

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Book Description :

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.

A Comprehensive Guide to Neural Network Modeling

A Comprehensive Guide to Neural Network Modeling Book
Author : Steffen Skaar
Publisher : Nova Science Publishers
Release : 2020-10-26
ISBN : 9781536185423
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Yury Tiumentsev,Mikhail Egorchev
Publisher : Academic Press
Release : 2019-05-17
ISBN : 0128154306
Language : En, Es, Fr & De

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Book Description :

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

Neural Network Modeling and Control

Neural Network Modeling and Control Book
Author : Zhengwei Wu
Publisher : Unknown
Release : 1999
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Neural Network Modeling and Control book written by Zhengwei Wu, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Neural Network Modeling and Control

Neural Network Modeling and Control Book
Author : Bernhard Eikens
Publisher : Unknown
Release : 1996
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Neural Network Modeling and Control book written by Bernhard Eikens, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Neural Networks for Cooperative Control of Multiple Robot Arms

Neural Networks for Cooperative Control of Multiple Robot Arms Book
Author : Shuai Li,Yinyan Zhang
Publisher : Springer
Release : 2017-10-29
ISBN : 9811070377
Language : En, Es, Fr & De

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Book Description :

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.

Neural Network Modeling and Control of a Gyro Mirror System

Neural Network Modeling and Control of a Gyro Mirror System Book
Author : Ching Ping Wong
Publisher : Unknown
Release : 1999
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Neural Network Modeling and Control of a Gyro Mirror System book written by Ching Ping Wong, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

DNA Computing Based Genetic Algorithm

DNA Computing Based Genetic Algorithm Book
Author : Jili Tao,Ridong Zhang,Yong Zhu
Publisher : Springer Nature
Release : 2020-07-01
ISBN : 981155403X
Language : En, Es, Fr & De

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Book Description :

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.

Application of Neural Networks to Modelling and Control

Application of Neural Networks to Modelling and Control Book
Author : G. F. Page,J. B. Gomm,D. Williams
Publisher : Unknown
Release : 1993
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Application of Neural Networks to Modelling and Control book written by G. F. Page,J. B. Gomm,D. Williams, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Neural Network Modeling and Control of a Flow Tank

Neural Network Modeling and Control of a Flow Tank Book
Author : Aaron W. Hart
Publisher : Unknown
Release : 1995
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Neural Network Modeling and Control of a Flow Tank book written by Aaron W. Hart, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Neural Network Modeling

Neural Network Modeling Book
Author : P. S. Neelakanta,Dolores DeGroff
Publisher : CRC Press
Release : 2018-02-06
ISBN : 1351428950
Language : En, Es, Fr & De

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Book Description :

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.

Identification of Nonlinear Systems Using Neural Networks and Polynomial Models

Identification of Nonlinear Systems Using Neural Networks and Polynomial Models Book
Author : Andrzej Janczak
Publisher : Springer Science & Business Media
Release : 2004-11-18
ISBN : 9783540231851
Language : En, Es, Fr & De

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Book Description :

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.

Knowledge based Artificial Neural Network for Process Modelling and Control

Knowledge based Artificial Neural Network for Process Modelling and Control Book
Author : Gary M. Scott
Publisher : Unknown
Release : 1993
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Knowledge based Artificial Neural Network for Process Modelling and Control book written by Gary M. Scott, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Artificial Neural Networks in Food Processing

Artificial Neural Networks in Food Processing Book
Author : Mohamed Tarek Khadir
Publisher : Walter de Gruyter GmbH & Co KG
Release : 2021-01-18
ISBN : 3110646056
Language : En, Es, Fr & De

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Book Description :

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 of Data Center

Neural Network Modeling and Control of Data Center Book
Author : Nachiket Rajendra Kansara
Publisher : Unknown
Release : 2016
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Data Center has become a definitive element of Modern IT infrastructure. With the development of high performance computing architectures and equipment, data centers consume large amount of electricity. Due to low Demand/Supply ratio of electricity production there is need to develop ways to reduce power footprint. Many researchers are working on approaches to resolve problems related to en- ergy usage of Data Center. One of these approaches is to develop a model-based control system that would control data centers in efficient way to reduce power footprint. Computational Fluid Dynamic (CFD) has been used to model the dynamic and complex environment of the data center. However, the drawback of this approach is its computational inefficiency. The effects of changing a single input may take an entire day to compute. Thus the CFD model is not well suited for model-based control. Instead we propose to use an Artificial Neural Network (ANN) model which predicts and control server temperatures in significantly less time.The Artificial Neural Network will be trained by using CFD data where first we will show that ANN can be used to predict temperature of data center servers.Both the steady state as well as transient data will be tested and then Neural Network model based controller will be used to control the temperature of data center.