Data processing: artificial intelligence – Neural network
Reexamination Certificate
2008-03-13
2011-11-22
Rivas, Omar Fernandez (Department: 2122)
Data processing: artificial intelligence
Neural network
C706S012000, C706S013000, C706S014000, C706S016000, C706S019000
Reexamination Certificate
active
08065244
ABSTRACT:
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.
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Chen Dingding
Hamid Syed
Stephenson Stanley
Zhong Allan
Fernandez Rivas Omar
Halliburton Energy Service,s Inc.
Krueger Daniel J.
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