Classification Coding and Image Recognition Based on Pulse Neural Network

Dong Li, Yiwen Jiao, Pengcheng Ge, Kuanfei Sun, Zefu Gao, Feilong Mao
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引用次数: 0

Abstract

Based on the third generation neural network spiking neural network, this paper optimizes and improves a classification and coding method, and proposes an image recognition method. Firstly, the read image is converted into a spike sequence, and then the spike sequence is encoded in groups and sent to the neurons in the spike neural network. After learning and training for many times, the quantization standard code is obtained. In this process, the spike sequence transformation matrix and dynamic weight matrix are obtained, and the unclassified data are output through the same matrix for image recognition and classification. Simulation results show that the above methods can get correct coding and preliminary recognition classification, and the spiking neural network can be applied.
基于脉冲神经网络的分类编码与图像识别
本文在第三代脉冲神经网络的基础上,对一种分类编码方法进行了优化和改进,提出了一种图像识别方法。首先将读取的图像转换成一个尖峰序列,然后对该尖峰序列进行分组编码,发送给尖峰神经网络中的神经元。经过多次学习和训练,得到了量化标准代码。在此过程中,得到尖峰序列变换矩阵和动态权矩阵,并通过同一矩阵输出未分类数据,用于图像识别和分类。仿真结果表明,上述方法能够得到正确的编码和初步的识别分类,可以应用峰值神经网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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