混沌优化神经网络方法及其在地震多属性研究中的应用  

A method of chaos optimization neural network and its application in multi-attribute seismic research

作　　者：安鸿伟[1] 纪大庆[2] 

机构地区：[1]中海石油研究中心 [2]中原油田采油一厂

出　　处：《中国海上油气》2008年第4期232-235,共4页China Offshore Oil and Gas

基　　金：国家自然科学基金项目(49874030)资助成果

摘　　要：针对传统的基于梯度下降法BP神经网络中存在非线性多极值目标函数易陷于局部最优解的问题,提出了一种权值学习混沌优化的神经网络方法。非线性动力系统具有初值敏感性、遍历性的特性,采用基于混沌和梯度反传训练相结合来训练网络,可以使网络的连接权在不断迭代过程中自适应演化。实际过井地震剖面地震多属性研究实践表明,所提出的混沌优化学习方法可以克服传统方法的不足,提高预测能力。In the traditional neural networks based on gradient decline, nonlinear object functions with multiple extrema are easy to get stuck in the problem of local optimization solution. Therefore, a new method of chaos optimization neural network with weight learning is introduced in this paper. Because of sensitive dependence on initial conditions and ergodicity of nonlinear dynamic systems, a combination of chaos with gradient backpropaga- tion can be used to train the neural network, by which the weight of a network may be in self-adaptive evolution during continuous iteration. A case of multi-attribute research on seismic sections across wells shows that this learning method of chaos optimization can avoid shortcomings of traditional approaches and improve prediction ability of a neural network.

关 键 词：混沌优化 神经网络 地震多属性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] P631.443[自动化与计算机技术—控制科学与工程]