A SIMPLIFIED NEURON MODEL AS A PRINCIPAL COMPONENT ANALYZER
A SIMPLIFIED NEURON MODEL AS A PRINCIPAL COMPONENT ANALYZER
复制标题
DOI：
10.1007/bf00275687
复制
发表时间：
1982-01-01
影响因子：
1.9
通讯作者：
OJA, E
中科院分区：
文献类型：
--
作者：
OJA, E
分析了一种简单的线性神经元模型，并推导了一类新的无约束学习规则。结果表明，模型神经元倾向于从平稳输入向量序列中提取主成分。
A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence.