Multistability and multiperiodicity analysis of complex-valued neural networks

Jin Hu, Jun Wang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Multistability and multiperiodicity of neual networks are usually considered in the application of associative memory. In this paper, we study the multistability and multiperiodicity of complexvalued neural networks (CVNNs for short) with one step piecewise linear activation functions. By separating the CVNN into its real and imaginary parts and using state decomposition, we can easily increase the storage capacity by using less neurons. Simulation results are given to illustrative the effectiveness of the theoretical results.

Original languageEnglish
Pages (from-to)59-68
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8866
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • Complex-valued neural networks
  • Multiperiodicity
  • Multistability

Fingerprint

Dive into the research topics of 'Multistability and multiperiodicity analysis of complex-valued neural networks'. Together they form a unique fingerprint.

Cite this