基于常微分方程的实时矢量量化和聚类。

IEEE transactions on neural networks Pub Date : 2011-12-01 Epub Date: 2011-10-31 DOI:10.1109/TNN.2011.2172627
Jie Cheng, Mohammad R Sayeh, Mehdi R Zargham, Qiang Cheng
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引用次数: 5

摘要

本文简要介绍了一种基于常微分方程的矢量量化或聚类的动态系统方法,具有实时实现的潜力。两个不同模式聚类的实例表明,该模型可以成功地量化不同类型的输入模式。此外,我们还分析和研究了动力系统的稳定性。通过发现某些输入模式的平衡点并分析其稳定性,我们给出了系统对其警戒参数的量化行为。提出的系统应用于两个现实世界的问题,提供了可比较的结果,最好的报告结果。这验证了我们提出的方法的有效性。
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Real-time vector quantization and clustering based on ordinary differential equations.

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.

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来源期刊
IEEE transactions on neural networks
IEEE transactions on neural networks 工程技术-工程:电子与电气
自引率
0.00%
发文量
2
审稿时长
8.7 months
期刊最新文献
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