【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,提出了一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
【机 构】
：
南京航空学院计算机系,南京航空学院计算机系,南京航空学院计算机系 南京 210016,南京 210016,南京 210016
论文部分内容阅读
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,提出了一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。
Based on the WISARD single-layer pattern recognition system in the UK, a new multi-layer adaptive pattern recognition system model (MAPR) is proposed by using the concept of sparse distributed storage (SDM) by P. Kanerva. And made a detailed description of its working process and main features, and also listed the preliminary test results of Chinese characters recognition of multi-body printed matter. The MAPR replaced the WISARD conventional RAM array with a sparse RAN array and replaces the original system’s direct placement strategy with a n-ary pattern-based training strategy. In addition to the system to maintain the important advantages of the original system, the performance of large dimensional or nondeterministic mode data recognition has been significantly improved.
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