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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 4
  3. Swarm Intelligence : Volume 4, Issue 1, March 2010
  4. Self-assembly of neural networks viewed as swarm intelligence
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 4, Issue 4, December 2010
Swarm Intelligence : Volume 4, Issue 3, September 2010
Swarm Intelligence : Volume 4, Issue 2, June 2010
Swarm Intelligence : Volume 4, Issue 1, March 2010
Self-assembly of neural networks viewed as swarm intelligence
Flocking for multi-robot systems via the Null-Space-based Behavioral control
A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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Self-assembly of neural networks viewed as swarm intelligence

Content Provider SpringerLink
Author Martin, Charles E. Reggia, James A.
Copyright Year 2009
Abstract While self-assembly is a fairly active area of research in swarm intelligence, relatively little attention has been paid to the issues surrounding the construction of network structures. In this paper we extend methods developed previously for controlling collective movements of agent teams to serve as the basis for self-assembly or “growth” of networks, using neural networks as a concrete application to evaluate our approach. Our central innovation is having network connections arise as persistent “trails” left behind moving agents, trails that are reminiscent of pheromone deposits made by agents in ant colony optimization models. The resulting network connections are thus essentially a record of agent movements. We demonstrate our model’s effectiveness by using it to produce two large networks that support subsequent learning of topographic and feature maps. Improvements produced by the incorporation of collective movements are also examined through computational experiments. These results indicate that methods for directing collective movements can be adopted to facilitate network self-assembly.
Starting Page 1
Ending Page 36
Page Count 36
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 4
Issue Number 1
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2009-10-21
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Swarm intelligence Self-assembly Neural network Developmental model Self-organizing map Computer Communication Networks Communications Engineering, Networks ApplicationMathematics/Computational Methods of Engineering Computer Systems Organization and Communication Networks Artificial Intelligence (incl. Robotics)
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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