Training a self-learning network using interpolated input sets based on a target output
US9342793B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Aug 31, 2010 |
| Grant date | May 17, 2016 |
| Priority date | — |
| Expiry date | Aug 24, 2032 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments relate to systems and methods for training a self-learning network using interpolated input sets based on a target output. A database management system can store sets of operational data, such as financial, medical, climate or other information. A user can input or access a set of target data, representing an output which a user wishes to be generated from an interpolated set of input data. The interpolation engine can generate a conformal interpolation function and input sets that map to the set of target output data. After interpolation, the interpolation engine can transmit the interpolated inputs, along with the set of target output data and other information, to a self-learning network such as a neural or fuzzy logic network. The self-learning network can be trained to converge to the target output based on the interpolated input results as generated by the interpolation engine, thus reproducing the desired interpolation function.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.