模糊神经网络在条形码识别中的应用

A FUZZY NEURAL NETWORK FOR BARCODE RECOGNITION

本文把模糊集合论的知识应用于自组织神经网络,提出一个模糊神经网络算法。新算法克服了一般神经网络方法共同面临的学习时间长、对网络参数敏感的弱点,广泛适用于一般的模式识别问题。对条形码识别问题的应用情况表明,新算法无论在网络学习的速度上还是在识别结果上都优于Kohonen的自组织神经网络方法。

Fuzzy set theory is adopted in a new neural computing algorithm. It has been proved that the new algorithm gets rid of two fatal defects facing most of the existing neural computing algorithms i.e. the slow training speed and network parameter sensibility. It is well suited to general pattern recognition tasks. Applications to the barcode recognition show that fuzzy neural computing is superior to the Koho-nen' s self-organizing neural computing in the sense of training speed and recognition results.