Competition-Based Neural Networks with Robotic Applications

  • Shuai Li
  • Long Jin

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Shuai Li, Long Jin
    Pages 25-55
  3. Shuai Li, Long Jin
    Pages 81-102

About this book


Focused on solving competition-based 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 competition-based problem-solving, 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.


Competition-based Neural Networks Distributed Network Computer Simulations Distributed Coordination Control Redundant Robots

Authors and affiliations

  • Shuai Li
    • 1
  • Long Jin
    • 2
  1. 1.The Hong Kong Polytechnic University Hong KongChina
  2. 2.Lanzhou University LanzhouChina

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-981-10-4946-0
  • Online ISBN 978-981-10-4947-7
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site