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Complex functional networks

Author

Listed:
  • Li, Chunguang
  • Liao, Xiaofeng
  • Wu, Zhongfu
  • Yu, Juebang

Abstract

Functional networks are a recently introduced extension of neural networks, which deal with general functional models instead of sigmoidal-like ones. In this paper, we propose complex functional networks, whose inputs, outputs, neural functions and arguments are all complex-valued. The general learning algorithm for this kind of complex functional networks is derived. And the performance of the proposed complex functional networks is demonstrated with application in the identification of complex-valued communication channels.

Suggested Citation

  • Li, Chunguang & Liao, Xiaofeng & Wu, Zhongfu & Yu, Juebang, 2001. "Complex functional networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 57(6), pages 355-365.
    • Handle: RePEc:eee:matcom:v:57:y:2001:i:6:p:355-365
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    Cited by:

    1. Feng, Shumin & Xin, Mengwei & Lv, Tianling & Hu, Baoyu, 2019. "A novel evolving model of urban rail transit networks based on the local-world theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    2. Shudong Li & Xiaobo Wu & Dawei Zhao & Aiping Li & Zhihong Tian & Xiaodong Yang, 2018. "An efficient dynamic ID-based remote user authentication scheme using self-certified public keys for multi-server environments," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.

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