Method and circuits for associating a norm to each component of an input pattern presented to a neural network
US6782373B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 12, 2001 |
| Grant date | Aug 24, 2004 |
| Priority date | — |
| Expiry date | Jan 6, 2023 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/24147
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The method and circuits of the present invention aim to associate a norm to each component of an input pattern presented to an input space mapping algorithm based artificial neural network (ANN) during the distance evaluation process. The set of norms, referred to as the “component” norms is memorized in specific memorization means in the ANN. In a first embodiment, the ANN is provided with a global memory, common for all the neurons of the ANN, that memorizes all the component norms. For each component of the input pattern, all the neurons perform the elementary (or partial) distance calculation with the corresponding prototype components stored therein during the distance evaluation process using the associated component norm. The distance elementary calculations are then combined using a “distance” norm to determine the final distance between the input pattern and the prototypes stored in the neurons. In another embodiment, the set of component norms is memorized in the neurons themselves in the prototype memorization means, so that the global memory is no longer physically necessary. This implementation allows to significantly optimize the consumed silic…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.