摘要
介绍了径向基函数(RBF)神经网络的原理、学习算法及其在地震预报专家系统ESEP 3.0中的应用。实际应用结果表明, 该神经网络可以很好地克服BP神经网络学习过程的收敛过分依赖于初值和可能出现局部收敛的缺陷, 具有较快的运算速度、较强的非线性映射能力和较好的预报效能。
the principle and algorithm of neural networks of Radial Basis Function (R BF) and its application to the expert system for earthquake prediction (ESEP 3.0) are introduced in this paper. The actual application in earthquake forecast shows that the neutral networks can overcome some demerit of BP neural networks in leaning process, the constringency excessively depend on initial value and optimization constringency and often can′t appear. The RBF neural networks possess the rapid operation speed in learning and strong nonlinear mapping ability and very good efficiency.
出处
《地震》
CSCD
北大核心
2005年第2期19-25,共7页
Earthquake
基金
"十五"科技部科技攻关项目(2001BA601B01 04 04)
