航空学报 > 1993, Vol. 14 Issue (2): 26-32

多层自适应模式识别系统模型

杨国庆, 陈松灿, 吕军   

  1. 南京航空学院计算机系,南京 210016
  • 收稿日期:1990-06-02 修回日期:1991-05-31 出版日期:1993-02-25 发布日期:1993-02-25

A MULTI-LAYER ADAPTIVE PATLERN RECOGNITION SYSTEM MODEL

Yang Guo-qing, Chen Song-can, Lu Jun   

  1. Depatment of Computer Nanjing Aeronautical Jnsfitute,Nanjing,210016
  • Received:1990-06-02 Revised:1991-05-31 Online:1993-02-25 Published:1993-02-25

摘要: 在英国WISARD单层模式识别系统的基础上,借助P.Kanerva的稀疏分布存贮(SDM)的概念,提出了~种新的多层自适应模式识别系统模型(MAPR)。并就其工作过程和主要特点作了较详细的叙述,还列出了多体印刷体汉字识别的初步试验结果。MAPR用稀疏RAN阵列代替WISARD的常规RAM阵列,用对n元模式计频的训练策略代替了原系统的直接置位策略。使系统除了保持原系统的重要优点外,在大维数或非确定性模式数据识别方面,其性能有了明显改善。

关键词: 模式识别, 稀疏分布存贮, 并行分布处理, 神经元网络

Abstract: Based-on a single-layer pattern recognition system (WISARD) and referred to the concept of a sparse distributed memory (SDM) by P.Kanerva, this paper presents a novel multi-layer adaptive pattern recognition system model and describes in detail its processing procedures and main features. The results of primitive multi-font printed Chinese character recognition experiment are given.Unlike WISARD, MAPR replaces the normal RAM array with sparse RAM array, and the directly setting bit strategy with counting frequency training for n-tuple pattern, so that not only MAPR maintains the main advantages of WISARD, but its performance is obviously improved as well in the aspect of recognizing large-dimensional or non-deterministic pattern data.

Key words: pattern recognition system, sparse distrobuted memory