Genetic algorithm based selection of neural network ensemble for processing well logging data
    41.
    发明申请
    Genetic algorithm based selection of neural network ensemble for processing well logging data 有权
    基于遗传算法的神经网络集合选择,用于处理测井数据

    公开(公告)号:US20050246297A1

    公开(公告)日:2005-11-03

    申请号:US10811403

    申请日:2004-03-26

    CPC classification number: G06N3/086

    Abstract: A system and method for generating a neural network ensemble. Conventional algorithms are used to train a number of neural networks having error diversity, for example by having a different number of hidden nodes in each network. A genetic algorithm having a multi-objective fitness function is used to select one or more ensembles. The fitness function includes a negative error correlation objective to insure diversity among the ensemble members. A genetic algorithm may be used to select weighting factors for the multi-objective function. In one application, a trained model may be used to produce synthetic open hole logs in response to inputs of cased hole log data.

    Abstract translation: 一种用于生成神经网络集合的系统和方法。 常规算法用于训练具有错误分集的多个神经网络,例如通过在每个网络中具有不同数量的隐藏节点。 使用具有多目标适应度函数的遗传算法来选择一个或多个合奏。 适应度函数包括负误差相关目标,以确保集合成员之间的多样性。 可以使用遗传算法来选择多目标函数的加权因子。 在一个应用中,可以使用经过训练的模型来响应于套管孔日志数据的输入来产生合成的开孔日志。

Patent Agency Ranking