摘要
在网络中同一隐层的所有神经元对不同样本的输出所构成的向量组应线性无关本文利用这一基本事实，对每一隐层引入了一相关向量及相应的无关度，根据无关度对该隐层神经元数目进行删除或增加，同时适当调整相应的网络权值，这样做既可以避免对隐层神经元的预先确定，同时还可以在学习过程中逃离局部极小根据删除神经元对网络所带来的误差的详细分析。
Vectors, which consist of the output of every neuron in same hidden layer corresponding to different samples, should be nonlinearly correlated. With this basic fact, this Paper firstly gives the definition of linearly correlated vector and corresponding nonlinear correlation measure for every hidden layer, then adds or deletes a neuron for a hidden layer according to its nonlinear correlation measure and adjusts the neural networks weight, values appropriately. This method can not only avoid confining the number of neuron units in a hidden layer, but also escape local minimum during the learnig process. According to error analysis in detail, if gives the optimistic rule of deleting neuron in hidden layer. Numerical experiments illustrate its efficiency.
出处
《电子学报》
EI
CAS
CSCD
北大核心
1998年第11期140-144,共5页
Acta Electronica Sinica
基金
中国科技大学研究生院院长择优基金
关键词
隐单元删除
删除规则
前向网络
动态学习
Deleting hidden neuron, Deleting rule, Forward network, Dynamic learning
