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Reliable Recognition of Handwritten Digits Using Hamming Network

机译:使用汉明网络可靠地识别手写数字

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This paper presents VLSI implementation of handwritten digit recognition system based on analog neural network. The recognition system is based on the least hamming distance neural network which both learning and classification. The circuit is simulated using SPICE tool at 180nm CMOS technology. The proposed circuit is modified version of existing circuit where initially first winner output voltage is set to logic one and rest all to logic zero and also determines next subsequent winner having minimum hamming distance. This type of circuit can be utilized by visual tracking system providing them ability to have backup recognition utility in case first recognized pattern proves to be incorrect. This design shows low power consumption of 34mW.
机译:本文介绍了基于模拟神经网络的手写数字识别系统的VLSI实现。识别系统基于学习和分类的最少的汉明距离神经网络。使用Spice工具在180nm CMOS技术中模拟电路。所提出的电路是经过修改的现有电路版本,其中最初首先首先赢家输出电压被设置为逻辑一个,并将全部恢复为逻辑零,并确定下一个后续赢家的最小汉明距离。这种类型的电路可以通过可视跟踪系统使用,以便在首先识别的模式被证明是不正确的情况下具有备份识别实用程序的能力。这种设计显示出34MW的低功耗。

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