Abstract
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 language | English |
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Article number | 6064899 |
Pages (from-to) | 2143-2148 |
Number of pages | 6 |
Journal | IEEE Transactions on Neural Networks |
Volume | 22 |
Issue number | 12 PART 1 |
DOIs | |
State | Published - Dec 2011 |
Keywords
- 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