摘要
针对BP网络学习速度的缓慢性,本文提出了一种联合优化后的快速学习算法。其改进具体表现在以下方面:1)采用Cauchy误差估计器代替传统的LMS误差估计器;2)对常规的Sigmoid函数引入形态因子:3)采用非单调线性搜索法实现学习步长的自适应变化。最后,本文以模式分类、函数逼近和数据压缩的典型应用为例分别与标准BP和常规改进算法进行比较,验证了该算法的优越性。
To accelerate the training speed of BP network, a joint-optimized fast BP learning algorithm is proposed. improvements can be achieved as following: 1) Conventional LMS error estimator is replaced by Cauchy error estimator: 2) Shape factor is adopted in Sigmoid function, 3) The adaptive changes of learning rate is realized by using non-sole linear search. Effectiveness of this algorithm is verified by comparison of performance of this algorithm with conventional modified and standard BP algorithms in typical BP network applications as pattern-classification, approximation of mathematical function and data compression.
出处
《电路与系统学报》
CSCD
2000年第3期26-30,共5页
Journal of Circuits and Systems
关键词
BP神经网络
学习算法
联合优化
BP neural networks
Cauchy error estimator
shape Factor
non-sole linear search