一种用于模式识别的新型开关电流Hamming神经网络

本文首次提出了一种用于模式识别的新型开关电流Ｈａｍｍｉｎｇ神经网络，它采用电流镜计算待识模式与标准模式的匹配度，然后，通过开关电流型排序电路进行匹配度的比较并输出识别结果，该Ｈａｍｍｉｎｇ神经网络可以按匹配度大小的顺序依次输出匹配度以及相应的标准模式，这将十分有利于改善系统的性能同时，该Ｈａｍｍｉｎｇ网络还可以进行绝对拒识和相对拒识的判断，这大大地提高了系统的可靠性，通过ＰＳＰＩＣＥ模拟，结果表明

A Novel Switched-Current Hamming Neural Network for Pattern Recognition

A novel switched-current Hamming neural network for pattern recognition is firstly proud. The calculating circuit for template matching is composed of current mirrors. Then levels of matching are compared in the switched-current sorter based on magnitude and the results are outputted.The pro-Hamming network is able to output all of levels of matching and stand patterns corresponding to these levels according to magnitude of levels. On the other hand, this network could realize the absolute rejection and relative rejection. This improves the reliability of the system. The structure of this network is simple, flexible and its scale can be easily extended. PSPICE simulation shows that the network has high resolution and high precision. Since switched-current structure is employed, this network is able to be directly fabricated in a standard digital CMOS process and easily implemented in VLSI technology.