Real-Time Vector Quantization and Clustering Based on Ordinary Differential Equations

Author(s)

Jie Cheng, University of Hawaii, Hilo
Mohammad R. Sayeh, Southern Illinois University Carbondale
Mehdi Zargham, University of DaytonFollow
Qiang Cheng, Southern Illinois University Carbondale

Document Type

Article

Publication Date

12-2011

Publication Source

IEEE Transactions on Neural Networks

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.

Inclusive pages

2143-2148

ISBN/ISSN

1045-9227

Comments

Permission documentation on file.

Copyright

Copyright © 2011, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publisher

Institute of Electrical and Electronics Engineers

Volume

22

Peer Reviewed

yes

Issue

12

eCommons Citation

Cheng, Jie; Sayeh, Mohammad R.; Zargham, Mehdi; and Cheng, Qiang, "Real-Time Vector Quantization and Clustering Based on Ordinary Differential Equations" (2011). Computer Science Faculty Publications. 159.
https://ecommons.udayton.edu/cps_fac_pub/159