Kalman-filtering using local interactions
| dc.creator | Poczos, Barnabas | |
| dc.creator | Lorincz, Andras | |
| dc.date | 2003-02-28 | |
| dc.date.accessioned | 2026-07-07T03:19:29Z | |
| dc.date.available | 2026-07-07T03:19:29Z | |
| dc.description | There is a growing interest in using Kalman-filter models for brain modelling. In turn, it is of considerable importance to represent Kalman-filter in connectionist forms with local Hebbian learning rules. To our best knowledge, Kalman-filter has not been given such local representation. It seems that the main obstacle is the dynamic adaptation of the Kalman-gain. Here, a connectionist representation is presented, which is derived by means of the recursive prediction error method. We show that this method gives rise to attractive local learning rules and can adapt the Kalman-gain. | |
| dc.identifier | https://arxiv.org/abs/cs/0302039 | |
| dc.identifier | http://arxiv.org/abs/cs/0302039 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/31481 | |
| dc.subject | Artificial Intelligence | |
| dc.subject | I.2.6 | |
| dc.title | Kalman-filtering using local interactions | |
| dc.type | text |