摘要
人工神经网络(ANN)中普遍采用的神经元模型具有单调有界的I/O特性,该文称其为近线性神经元(ALN,approximate-linear neuron),ALN既简化了网络的复杂性,也是限制ANN能力的一个瓶径.通过比较Hopfield神经网络的稳定性和Conway生命游戏的复杂行为,该文提出非线性神经元(NLN,non-linear neuron)的概念,其I/O特性是完全非线性的.ALN可以进一步提高ANN的非线性,从而增强它的功能,余数制神经网络是一个例子.
: In an artificial neural network,the property of the neuron model normally is monotone and bounded.This paper names it as approximate-linear neuron(ALN).It simplifies the complexity of the neural network,at the same time,becomes a bottleneck that limits the network's abilities.In contrast,by comparing the stability of Hopfield neural network with the aplenty behavior of Conway's life game,this paper preferred a new concept,non-linear neuron(NLN),which I/O property is non-linear completely.NLN can enhance the non-linear properties of ANN,so an ANN with NLN will be more powerful.Remainder ANN is an example.
出处
《计算机工程与应用》
CSCD
北大核心
2000年第10期56-58,67,共4页
Computer Engineering and Applications
关键词
近线性神经元
非线性神经元
HOPFIELD神经网络
: approximate-linear neuron(ALN),non-linear neuron(NLN),Hopfield neural network,life game