Competition based on selective positive-negative feedback

Shuai Li, Long Jin

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review


In this chapter, we make steps in that direction and present a simple model, which produces the winner-take-all competition by taking advantage of selective positive-negative feedback through the interaction of neurons via p-norm. Compared to models presented in Chaps. 1, 2 and 3, this model has an explicit explanation of the competition mechanism. The ultimate convergence behavior of this model is proven analytically. The convergence rate is also discussed. Simulations are conducted in the static competition and the dynamic competition scenarios. Both theoretical and numerical results validate the effectiveness of the dynamic equation in describing the nonlinear phenomena of winner-take-all competition.

Original languageEnglish
Title of host publicationSpringerBriefs in Applied Sciences and Technology
Number of pages23
Publication statusPublished - 1 Jan 2018

Publication series

NameSpringerBriefs in Applied Sciences and Technology
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318


  • Global stability
  • Numerical simulations
  • Recurrent neural networks
  • Selective positive-negative feedback
  • Winner-take-all competition

ASJC Scopus subject areas

  • Biotechnology
  • Chemical Engineering(all)
  • Mathematics(all)
  • Materials Science(all)
  • Energy Engineering and Power Technology
  • Engineering(all)


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