线性可分结构系对二进神经元的覆盖问题  

Cover Problem of Linear Separable Structures to Binary Neurons

作　　者：杨娟[1] 陆阳[1] 黄镇谨[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009

出　　处：《计算机科学》2012年第7期195-199,共5页Computer Science

基　　金：国家自然科学基金(60873195;61070220);高等学校博士点基金(20090111110002)资助

摘　　要：二进神经网络中每个二进神经元等价于一个线性可分函数,但每个二进神经元所表达的线性可分函数的逻辑意义仍不完全清楚。对此,首先分析了已有的几种线性可分结构系;其次,讨论了其是否覆盖了所有的二进神经元;最后,指出阈值在某些范围内二进神经元所对应的线性可分函数的逻辑意义仍不清楚,这为进一步完善二进神经元的覆盖问题指明了方向。In binary neural networks, every neuron is equivalent to a linear separable function, however, the logical meaning of every linear separable function which is expressed by the binary neuron is still not clear. This paper firstly analysed the known linear separable structures, then discussed whether these known structures cover the whole binary neurons, finally pointed out when the threshold value is in some range, the logical meaning of the linear separable func- tion is still unclear. This result provides the way of the cover problem of binary neurons.

关 键 词：二进神经网络 线性可分结构系 覆盖问题 逻辑意义 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]