Real-time vector quantization and clustering based on ordinary differential equations

Jie Cheng, Mohammad R. Sayeh, Mehdi R. Zargham, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This brief presents a dynamical system approach to vector quantization or clustering based on ordinary differential equations with the potential for real-time implementation. Two examples of different pattern clusters demonstrate that the model can successfully quantize different types of input patterns. Furthermore, we analyze and study the stability of our dynamical system. By discovering the equilibrium points for certain input patterns and analyzing their stability, we have shown the quantizing behavior of the system with respect to its vigilance parameter. The proposed system is applied to two real-world problems, providing comparable results to the best reported findings. This validates the effectiveness of our proposed approach.

Original languageEnglish
Article number6064899
Pages (from-to)2143-2148
Number of pages6
JournalIEEE Transactions on Neural Networks
Issue number12 PART 1
StatePublished - Dec 2011


  • Neural networks
  • ordinary differential equation-based clustering
  • real-time clustering
  • vector quantization

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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