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A pruning feedforward small-world neural network based on Katz centrality for nonlinear system modeling

  • Published:2020-10 Issue: Volume:130 Page:269-285
  • ISSN:0893-6080
  • Container-title:Neural Networks
  • language:en
  • Short-container-title:Neural Networks

Author:

Li WenjingORCID,Chu Minghui,Qiao Junfei

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation, China

Beijing Municipal Education Commission Foundation, China

National Key Research and Development Project, China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference35 articles.

1. Pruning algorithms of neural networks - a comparative study;Augasta;Central European Journal of Computer Science,2013

2. Complex brain networks: graph theoretical analysis of structural and functional systems;Bullmore;Nature Reviews Neuroscience,2009

3. A novel memristive multilayer feedforward small-world neural network with its applications in PID control;Dong;The Scientific World Journal,2014

4. Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes;Erkaymaz;Chaos, Solitons & Fractals,2016

5. Performance analysis of a feed-forward artifical neural network with small-world topology;Erkaymaz;Procedia Technology,2012

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