Genetic algorithm based selection of neural network ensemble for processing well logging data
US7280987B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 26, 2004 |
| Grant date | Oct 9, 2007 |
| Priority date | — |
| Expiry date | Jul 25, 2025 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/086
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.