Method for the computer-aided learning of a recurrent neural network for modeling a dynamic system
US9235800B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 12, 2011 |
| Grant date | Jan 12, 2016 |
| Priority date | — |
| Expiry date | May 28, 2032 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.