A modified current mode Hamming neural network for totally unconstrained handwritten numeral recognition

被引：0
|
作者：
Li, GX [1 ]
Shi, BX [1 ]
Lu, W [1 ]
机构：
[1] Tsing Hua Univ, Inst Microelect, Beijing 100084, Peoples R China
来源：
IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE | 1998年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A compact smart current mode Hamming neural network for classifying complex pattern such as totally unconstrained handwritten digit is presented in this paper. It is based on multi-threshold template matching, multi-stage matching and k-WTA(k-Winner-Taker-All), some different from general Hamming neural network. The neural classifier consists of two kinds of templates : one is binary template and another is multi-value programmable templates, each of them has its own threshold and realized in MOS current mirrors, the current mode k-WTA which is reconfigurable is put forward, The second stage matching templates are programmable from outside of the chip. This mixed analog-digital Hamming neural classifier can be fabricated in a standard digital CMOS technology.
引用
收藏
页码：1857 / 1860
页数：4
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