The Resource CompetitionBased Neural Networks with Robotic Applications
CompetitionBased Neural Networks with Robotic Applications
Resource Information
The item CompetitionBased Neural Networks with Robotic Applications represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item CompetitionBased Neural Networks with Robotic Applications represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.
This item is available to borrow from 1 library branch.
 Summary
 Focused on solving competitionbased problems, this book designs, proposes, develops, analyzes and simulates various neural network models depicted in centralized and distributed manners. Specifically, it defines four different classes of centralized models for investigating the resultant competition in a group of multiple agents. With regard to distributed competition with limited communication among agents, the book presents the first distributed WTA (Winners Take All) protocol, which it subsequently extends to the distributed coordination control of multiple robots. Illustrations, tables, and various simulative examples, as well as a healthy mix of plain and professional language, are used to explain the concepts and complex principles involved. Thus, the book provides readers in neurocomputing and robotics with a deeper understanding of the neural network approach to competitionbased problemsolving, offers them an accessible introduction to modeling technology and the distributed coordination control of redundant robots, and equips them to use these technologies and approaches to solve concrete scientific and engineering problems
 Language
 eng
 Extent
 1 online resource (132 pages).
 Contents

 Preface; Acknowledgements; Contents; 1 Competition Aided with DiscreteTime Dynamic Feedback; 1.1 Introduction; 1.2 Problem Definition; 1.3 Model Formulation; 1.4 Theoretical Results; 1.5 Illustrative Examples; 1.5.1 DiscreteTime Static Competition; 1.5.2 DiscreteTime Dynamic Competition; 1.6 Summary; References; 2 Competition Aided with ContinuousTime Nonlinear Model; 2.1 Introduction; 2.2 The Model; 2.3 Theoretical Analysis and Results; 2.4 Illustrative Examples; 2.4.1 Static Competition; 2.4.2 Dynamic Competition; 2.5 Summary; References
 3 Competition Aided with FiniteTime Neural Network3.1 Introduction; 3.2 Model Description; 3.3 Convergence Analysis; 3.4 An Illustrative Example; 3.4.1 Accuracy; 3.4.2 Convergence Speed; 3.4.3 Comparisons on Computational Efficiency in Numerical Simulations; 3.4.4 Sensitivity to Additive Noise; 3.4.5 Robustness Against Time Delay; 3.4.6 Discussion; 3.5 Solving kWTA with the Proposed Neural Network; 3.5.1 Quadratic Programming Formulation for kTWA; 3.5.2 Theoretical Results for Solving kWTA with the Proposed Neural Network; 3.5.3 kWTA Simulations; 3.6 Summary; References
 4 Competition Based on Selective PositiveNegative Feedback4.1 Introduction; 4.2 Preliminaries; 4.3 The WinnerTakeAll Neural Network; 4.3.1 The Neural Network Based WinnerTakeAll Problem; 4.3.2 NeuroDynamics; 4.4 Convergence Results; 4.5 Discussion on OneSided Competition Versus CloselyMatched Competition; 4.6 Simulation Examples; 4.6.1 Static Competition; 4.6.2 Dynamic Competition; 4.7 Summary; References; 5 Distributed Competition in Dynamic Networks; 5.1 Introduction; 5.2 Problem Definition: Distributed WTA on Graphs; 5.3 Distributed WTA Protocol; 5.3.1 Basic Properties
 5.4 Convergence Analysis5.4.1 Global Convergence to the Equilibrium Point Set; 5.4.2 Instability of NonWTA Solutions; 5.4.3 Global Stability of the WTA Solution; 5.5 Numerical Validation; 5.6 Summary; References; 6 CompetitionBased Distributed Coordination Control of Robots; 6.1 Introduction; 6.2 Preliminary and Problem Formulation; 6.2.1 Redundant Robot Manipulator; 6.2.2 Problem Definitions and Assumptions; 6.3 Dynamic Task Allocation with Limited Communications; 6.4 Illustrative Example; 6.5 Summary; References
 Isbn
 9789811049477
 Label
 CompetitionBased Neural Networks with Robotic Applications
 Title
 CompetitionBased Neural Networks with Robotic Applications
 Language
 eng
 Summary
 Focused on solving competitionbased problems, this book designs, proposes, develops, analyzes and simulates various neural network models depicted in centralized and distributed manners. Specifically, it defines four different classes of centralized models for investigating the resultant competition in a group of multiple agents. With regard to distributed competition with limited communication among agents, the book presents the first distributed WTA (Winners Take All) protocol, which it subsequently extends to the distributed coordination control of multiple robots. Illustrations, tables, and various simulative examples, as well as a healthy mix of plain and professional language, are used to explain the concepts and complex principles involved. Thus, the book provides readers in neurocomputing and robotics with a deeper understanding of the neural network approach to competitionbased problemsolving, offers them an accessible introduction to modeling technology and the distributed coordination control of redundant robots, and equips them to use these technologies and approaches to solve concrete scientific and engineering problems
 Cataloging source
 EBLCP
 http://library.link/vocab/creatorName
 Li, Shuai
 Dewey number
 620
 Index
 no index present
 LC call number
 TA12040
 Literary form
 non fiction
 Nature of contents
 dictionaries
 http://library.link/vocab/relatedWorkOrContributorName
 Jin, Long
 Series statement
 SpringerBriefs in Applied Sciences and Technology
 http://library.link/vocab/subjectName

 Neural networks
 Robotics
 Label
 CompetitionBased Neural Networks with Robotic Applications
 Bibliography note
 Includes bibliographical references
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents

 Preface; Acknowledgements; Contents; 1 Competition Aided with DiscreteTime Dynamic Feedback; 1.1 Introduction; 1.2 Problem Definition; 1.3 Model Formulation; 1.4 Theoretical Results; 1.5 Illustrative Examples; 1.5.1 DiscreteTime Static Competition; 1.5.2 DiscreteTime Dynamic Competition; 1.6 Summary; References; 2 Competition Aided with ContinuousTime Nonlinear Model; 2.1 Introduction; 2.2 The Model; 2.3 Theoretical Analysis and Results; 2.4 Illustrative Examples; 2.4.1 Static Competition; 2.4.2 Dynamic Competition; 2.5 Summary; References
 3 Competition Aided with FiniteTime Neural Network3.1 Introduction; 3.2 Model Description; 3.3 Convergence Analysis; 3.4 An Illustrative Example; 3.4.1 Accuracy; 3.4.2 Convergence Speed; 3.4.3 Comparisons on Computational Efficiency in Numerical Simulations; 3.4.4 Sensitivity to Additive Noise; 3.4.5 Robustness Against Time Delay; 3.4.6 Discussion; 3.5 Solving kWTA with the Proposed Neural Network; 3.5.1 Quadratic Programming Formulation for kTWA; 3.5.2 Theoretical Results for Solving kWTA with the Proposed Neural Network; 3.5.3 kWTA Simulations; 3.6 Summary; References
 4 Competition Based on Selective PositiveNegative Feedback4.1 Introduction; 4.2 Preliminaries; 4.3 The WinnerTakeAll Neural Network; 4.3.1 The Neural Network Based WinnerTakeAll Problem; 4.3.2 NeuroDynamics; 4.4 Convergence Results; 4.5 Discussion on OneSided Competition Versus CloselyMatched Competition; 4.6 Simulation Examples; 4.6.1 Static Competition; 4.6.2 Dynamic Competition; 4.7 Summary; References; 5 Distributed Competition in Dynamic Networks; 5.1 Introduction; 5.2 Problem Definition: Distributed WTA on Graphs; 5.3 Distributed WTA Protocol; 5.3.1 Basic Properties
 5.4 Convergence Analysis5.4.1 Global Convergence to the Equilibrium Point Set; 5.4.2 Instability of NonWTA Solutions; 5.4.3 Global Stability of the WTA Solution; 5.5 Numerical Validation; 5.6 Summary; References; 6 CompetitionBased Distributed Coordination Control of Robots; 6.1 Introduction; 6.2 Preliminary and Problem Formulation; 6.2.1 Redundant Robot Manipulator; 6.2.2 Problem Definitions and Assumptions; 6.3 Dynamic Task Allocation with Limited Communications; 6.4 Illustrative Example; 6.5 Summary; References
 Dimensions
 unknown
 Extent
 1 online resource (132 pages).
 Form of item
 online
 Isbn
 9789811049477
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9789811049477
 Specific material designation
 remote
 System control number

 ocn989818971
 (OCoLC)989818971
 Label
 CompetitionBased Neural Networks with Robotic Applications
 Bibliography note
 Includes bibliographical references
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents

 Preface; Acknowledgements; Contents; 1 Competition Aided with DiscreteTime Dynamic Feedback; 1.1 Introduction; 1.2 Problem Definition; 1.3 Model Formulation; 1.4 Theoretical Results; 1.5 Illustrative Examples; 1.5.1 DiscreteTime Static Competition; 1.5.2 DiscreteTime Dynamic Competition; 1.6 Summary; References; 2 Competition Aided with ContinuousTime Nonlinear Model; 2.1 Introduction; 2.2 The Model; 2.3 Theoretical Analysis and Results; 2.4 Illustrative Examples; 2.4.1 Static Competition; 2.4.2 Dynamic Competition; 2.5 Summary; References
 3 Competition Aided with FiniteTime Neural Network3.1 Introduction; 3.2 Model Description; 3.3 Convergence Analysis; 3.4 An Illustrative Example; 3.4.1 Accuracy; 3.4.2 Convergence Speed; 3.4.3 Comparisons on Computational Efficiency in Numerical Simulations; 3.4.4 Sensitivity to Additive Noise; 3.4.5 Robustness Against Time Delay; 3.4.6 Discussion; 3.5 Solving kWTA with the Proposed Neural Network; 3.5.1 Quadratic Programming Formulation for kTWA; 3.5.2 Theoretical Results for Solving kWTA with the Proposed Neural Network; 3.5.3 kWTA Simulations; 3.6 Summary; References
 4 Competition Based on Selective PositiveNegative Feedback4.1 Introduction; 4.2 Preliminaries; 4.3 The WinnerTakeAll Neural Network; 4.3.1 The Neural Network Based WinnerTakeAll Problem; 4.3.2 NeuroDynamics; 4.4 Convergence Results; 4.5 Discussion on OneSided Competition Versus CloselyMatched Competition; 4.6 Simulation Examples; 4.6.1 Static Competition; 4.6.2 Dynamic Competition; 4.7 Summary; References; 5 Distributed Competition in Dynamic Networks; 5.1 Introduction; 5.2 Problem Definition: Distributed WTA on Graphs; 5.3 Distributed WTA Protocol; 5.3.1 Basic Properties
 5.4 Convergence Analysis5.4.1 Global Convergence to the Equilibrium Point Set; 5.4.2 Instability of NonWTA Solutions; 5.4.3 Global Stability of the WTA Solution; 5.5 Numerical Validation; 5.6 Summary; References; 6 CompetitionBased Distributed Coordination Control of Robots; 6.1 Introduction; 6.2 Preliminary and Problem Formulation; 6.2.1 Redundant Robot Manipulator; 6.2.2 Problem Definitions and Assumptions; 6.3 Dynamic Task Allocation with Limited Communications; 6.4 Illustrative Example; 6.5 Summary; References
 Dimensions
 unknown
 Extent
 1 online resource (132 pages).
 Form of item
 online
 Isbn
 9789811049477
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9789811049477
 Specific material designation
 remote
 System control number

 ocn989818971
 (OCoLC)989818971
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