摘要
传统的神经网络 ,如BP网络设计 ,不仅工作效率降低 ,网络性能低下 ,而且会因非线性多极值目标函数而陷于局部最优解。本文采用全局寻优的遗传算法 (GA)来辅助网络设计 ,实现网络结构、连接权及学习规则的自适应演化。通过利用测井资料与孔隙度参数的学习建模 ,表明该方法可以克服传统方法的不足 ,具有一定的推广应用价值。
The conventional neural network design method, such as BP algorithm, its topological construction and parameters is determined by designer's experience and repeated test. This not only lead to low work efficiency and poor network performance, but also usually lost in local optimal solution because of nonlinear multi_extreme object function. In this paper, we designed the network construction using genetic algorithm as an aid method, and determined the topology, linking weight and learn factor with adaptive evolution. Its application to porosity learning with log data show that this method can improve the network's performance and is a valuable method.
出处
《断块油气田》
CAS
2000年第4期41-42,共2页
Fault-Block Oil & Gas Field
关键词
遗传算法
孔隙度
建模
神经网络设计
GA
油气田
Neural network design, Genetic algorithm, Adaptive evolution, Porosity, Learning.
