Data compression by recurrent neural network dynamics

被引：0
|
作者：
Li, LK
机构：
来源：
1996 IEEE TENCON - DIGITAL SIGNAL PROCESSING APPLICATIONS PROCEEDINGS, VOLS 1 AND 2 | 1996年
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Recently, data compressions are dominated by the Fourier or wavelet transforms which approximate the given function or sequence as a linear sum of the basis functions. In this paper, we discuss tile use of dynamical systems for compression. Since Leaky-integrator model neural net can approximate arbitrary finite sequence, we propose bet compress a 'not too wild' signal by a recurrent neural network. As an initial valued problem, the information to be stored is the parameters of the system and the initial states. Elementary analysis on error Rad compression ratio are also given.
引用
收藏
页码：96 / 101
页数：6
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