摘要
在文［８］的工作基础上，利用模型逼近度和训练误差函数值相对误差函数梯度向量长度的变化率，给出前馈神经网络的一个自适应学习算法对一具体的分类问题进行计算，结果表明本文算法效果良好。
On the basis of the paper (8),we present a self-adaptive learning algorithm for a feedforward neural network by making use of the model approximation degree and the variation ratio of error function to its gradient norm The algorithm is tested on Fish′s plant classification problem and good results are achieved, improving that given in paper (8)
出处
《福州大学学报（自然科学版）》
CAS
CSCD
1998年第2期22-24,共3页
Journal of Fuzhou University(Natural Science Edition)
基金
国家863计划
福建省自然科学基金
关键词
前馈神经网络
模型逼近度
相对变化率
学习算法
feedforward neural network
model approximation degree
relative variation ratio
learning algorithm
