Neural Networks for Modelling and Control of Dynamic Systems

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  • Author : M. Norgaard
  • Publisher : Anonim
  • Pages : 246 pages
  • ISBN :
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKNeural Networks for Modelling and Control of Dynamic 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 : 31 December 1995
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

Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems
  • Author : Yury 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

Dynamic Interactions in Neural Networks: Models and Data

Dynamic Interactions in Neural Networks: Models and Data
  • Author : Michael A. Arbib,Shun-ichi Amari
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKDynamic Interactions in Neural Networks: Models and Data

This is an exciting time. The study of neural networks is enjoying a great renaissance, both in computational neuroscience - the development of information processing models of living brains - and in neural computing - the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume, Dynamic Interactions in Neural Networks: Models and Data can be given two interpretations. We present models and data on the dynamic interactions occurring in the brain, and

Neural Networks for Control

Neural Networks for Control
  • Author : W. Thomas Miller,Paul J. Werbos,Richard S. Sutton
  • Publisher : MIT Press
  • Release : 07 May 1995
GET THIS BOOKNeural Networks for Control

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

Recent Advances of Neural Network Models and Applications

Recent Advances of Neural Network Models and Applications
  • Author : Simone Bassis,Anna Esposito,Francesco Carlo Morabito
  • Publisher : Springer Science & Business Media
  • Release : 19 December 2013
GET THIS BOOKRecent Advances of Neural Network Models and Applications

This volume collects a selection of contributions which has been presented at the 23rd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Vietri sul Mare, Salerno, Italy during May 23-24, 2013. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop- is organized in two main components,

Neural Networks for Identification, Prediction and Control

Neural Networks for Identification, Prediction and Control
  • Author : Duc T. Pham,Xing Liu
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKNeural Networks for Identification, Prediction and Control

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

Fuzzy Systems

Fuzzy Systems
  • Author : Hung T. Nguyen,Michio Sugeno
  • Publisher : Springer Science & Business Media
  • Release : 31 July 1998
GET THIS BOOKFuzzy Systems

The analysis and control of complex systems have been the main motivation for the emergence of fuzzy set theory since its inception. It is also a major research field where many applications, especially industrial ones, have made fuzzy logic famous. This unique handbook is devoted to an extensive, organized, and up-to-date presentation of fuzzy systems engineering methods. The book includes detailed material and extensive bibliographies, written by leading experts in the field, on topics such as: Use of fuzzy logic

Introduction to Neural and Cognitive Modeling

Introduction to Neural and Cognitive Modeling
  • Author : Daniel S. Levine
  • Publisher : Psychology Press
  • Release : 01 February 2000
GET THIS BOOKIntroduction to Neural and Cognitive Modeling

This thoroughly, thoughtfully revised edition of a very successful textbook makes the principles and the details of neural network modeling accessible to cognitive scientists of all varieties as well as to others interested in these models. Research since the publication of the first edition has been systematically incorporated into a framework of proven pedagogical value. Features of the second edition include: * A new section on spatiotemporal pattern processing * Coverage of ARTMAP networks (the supervised version of adaptive resonance networks) and

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

Neural Network Control Of Robot Manipulators And Non-Linear Systems

Neural Network Control Of Robot Manipulators And Non-Linear Systems
  • Author : F W Lewis,S. Jagannathan,A Yesildirak
  • Publisher : CRC Press
  • Release : 14 August 2020
GET THIS BOOKNeural Network Control Of Robot Manipulators And Non-Linear Systems

There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor dynamics. The first chapter provides a background on neural networks and the second on dynamical systems and control. Chapter three introduces the