发明名称 |
Neural-network based surrogate model construction methods and applications thereof |
摘要 |
Various neural-network based surrogate model construction methods are disclosed herein, along with various applications of such models. Designed for use when only a sparse amount of data is available (a "sparse data condition"), some embodiments of the disclosed systems and methods: create a pool of neural networks trained on a first portion of a sparse data set; generate for each of various multi-objective functions a set of neural network ensembles that minimize the multi-objective function; select a local ensemble from each set of ensembles based on data not included in said first portion of said sparse data set; and combine a subset of the local ensembles to form a global ensemble. This approach enables usage of larger candidate pools, multi-stage validation, and a comprehensive performance measure that provides more robust predictions in the voids of parameter space. |
申请公布号 |
GB2462380(A) |
申请公布日期 |
2010.02.10 |
申请号 |
GB20090016094 |
申请日期 |
2008.03.13 |
申请人 |
HALLIBURTON ENERGY SERVICES, INC. |
发明人 |
DINGDING CHEN;ALLAN ZHONG;SYED HAMID;STANLEY STEPHENSON |
分类号 |
G06N3/04;G06F17/50;G06N3/10 |
主分类号 |
G06N3/04 |
代理机构 |
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代理人 |
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主权项 |
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地址 |
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