Artificial Neural Networks for Modelling and Control of Non Linear Systems

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.

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  • Author : Johan A.K. Suykens
  • Publisher : Springer Science & Business Media
  • Pages : 235 pages
  • ISBN : 1475724934
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKArtificial Neural Networks for Modelling and Control of Non Linear Systems

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
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKArtificial Neural Networks for Modelling and Control of Non-Linear Systems

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

Gas Turbines Modeling, Simulation, and Control

Gas Turbines Modeling, Simulation, and Control
  • Author : Hamid Asgari,XiaoQi Chen
  • Publisher : CRC Press
  • Release : 16 October 2015
GET THIS BOOKGas Turbines Modeling, Simulation, and Control

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

Neural Networks for Control

Neural Networks for Control
  • Author : W. Thomas Miller,Richard S. Sutton,Paul J. Werbos
  • Publisher : MIT Press
  • Release : 28 June 1995
GET THIS BOOKNeural Networks for Control

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

Fuzzy Neural Networks for Real Time Control Applications

Fuzzy Neural Networks for Real Time Control Applications
  • Author : Erdal Kayacan,Mojtaba Ahmadieh Khanesar
  • Publisher : Butterworth-Heinemann
  • Release : 07 October 2015
GET THIS BOOKFuzzy Neural Networks for Real Time Control Applications

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

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
  • Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
  • Publisher : Academic Press
  • Release : 15 March 2019
GET THIS BOOKArtificial Neural Networks for Engineering Applications

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

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
  • Publisher : Springer Science & Business Media
  • Release : 24 June 2008
GET THIS BOOKArtificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes

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

Neural Networks Modeling and Control

Neural Networks Modeling and Control
  • Author : Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
  • Publisher : Academic Press
  • Release : 15 January 2020
GET THIS BOOKNeural Networks Modeling and Control

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

Fundamentals of Neural Network Modeling

Fundamentals of Neural Network Modeling
  • Author : Randolph W. Parks,Daniel S. Levine,Debra L. Long
  • Publisher : MIT Press
  • Release : 28 June 1998
GET THIS BOOKFundamentals of Neural Network Modeling

Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians,

Semi-empirical Neural Network Modeling and Digital Twins Development

Semi-empirical Neural Network Modeling and Digital Twins Development
  • Author : Dmitriy Tarkhov,Alexander Nikolayevich Vasilyev
  • Publisher : Academic Press
  • Release : 23 November 2019
GET THIS BOOKSemi-empirical Neural Network Modeling and Digital Twins Development

Semi-empirical Neural Network Modeling presents a new approach on how to quickly construct an accurate, multilayered neural network solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single-layered neural networks built on the finite element method (FEM). The strength of the new method presented in this book is the automatic inclusion of task parameters in the final solution formula, which eliminates the need for repeated problem-solving. This is especially important for constructing

Neural Systems for Control

Neural Systems for Control
  • Author : Omid Omidvar,David L. Elliott
  • Publisher : Elsevier
  • Release : 24 February 1997
GET THIS BOOKNeural Systems for Control

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

Neural Networks in Bioprocessing and Chemical Engineering

Neural Networks in Bioprocessing and Chemical Engineering
  • Author : D. R. Baughman,Y. A. Liu
  • Publisher : Academic Press
  • Release : 28 June 2014
GET THIS BOOKNeural Networks in Bioprocessing and Chemical Engineering

Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice

Neural Network Modeling

Neural Network Modeling
  • Author : P. S. Neelakanta,Dolores DeGroff
  • Publisher : CRC Press
  • Release : 06 February 2018
GET THIS BOOKNeural Network Modeling

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.

Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems
  • Author : Yuri Tiumentsev,Mikhail Egorchev
  • Publisher : Academic Press
  • Release : 17 May 2019
GET THIS BOOKNeural Network Modeling and Identification of Dynamical Systems

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