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Published in:
Competition Aided with Discrete-Time Dynamic Feedback Read chapter Read first chapter

2018 | OriginalPaper | Chapter

4. Competition Based on Selective Positive-Negative Feedback

Authors : Shuai Li, Long Jin

Published in: Competition-Based Neural Networks with Robotic Applications

Publisher: Springer Singapore

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Abstract

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.

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previous chapter Competition Aided with Finite-Time Neural Network
next chapter Distributed Competition in Dynamic Networks
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Metadata
Title
Competition Based on Selective Positive-Negative Feedback
Authors
Shuai Li
Long Jin
Copyright Year
2018
Publisher
Springer Singapore
Book
Competition-Based Neural Networks with Robotic Applications

Print ISBN: 978-981-10-4946-0

Electronic ISBN: 978-981-10-4947-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-4947-7_4

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