Real-Time Vector Quantization and Clustering Based on Ordinary Differential Equations
IEEE Transactions on Neural Networks
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.
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.
Institute of Electrical and Electronics Engineers
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.