混沌优化神经网络方法及其在地震多属性研究中的应用
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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 backpropagation 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.
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 backpropagation 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.
引文
[1]彭真明,安鸿伟.演化神经网络学习方法及其应用[J].石油地球物理勘探,2001,4(2):193-197.
[2]KAPLAN D,GLASS L.Understanding nonlinear dynamics[M].World Books Publishing Company,1997.
[3]AIHARA K,TAKABE T,TOYODA M.Chaotic neural net-works[J].Physics Letter A,1990,144(6):333-340.
[4]安鸿伟,李正文.基于非线性动力学的地震反演方法[J].中国海上油气,2004,16(2):137-139.
[5]安鸿伟.混沌动力学与地震油储信息检测方法[D].成都:成都理工大学,2002.
[2]KAPLAN D,GLASS L.Understanding nonlinear dynamics[M].World Books Publishing Company,1997.
[3]AIHARA K,TAKABE T,TOYODA M.Chaotic neural net-works[J].Physics Letter A,1990,144(6):333-340.
[4]安鸿伟,李正文.基于非线性动力学的地震反演方法[J].中国海上油气,2004,16(2):137-139.
[5]安鸿伟.混沌动力学与地震油储信息检测方法[D].成都:成都理工大学,2002.
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