N-dimensional coulomb neural network which provides for cumulative learning of internal representations
US4897811A · kind A · utility
Assignee
Inventor
Key dates
| Filing date | Jan 19, 1988 |
| Grant date | Jan 30, 1990 |
| Priority date | — |
| Expiry date | Jan 19, 2008 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A learning algorithm for the N-dimensional Coulomb network is disclosed which is applicable to multi-layer networks. The central concept is to define a potential energy of a collection of memory sites. Then each memory site is an attractor of other memory sites. With the proper definition of attractive and repulsive potentials between various memory sites, it is possible to minimize the energy of the collection of memories. By this method, internal representations may be "built-up" one layer at a time. Following the method of Bachmann et al. a system is considered in which memories of events have already been recorded in a layer of cells. A method is found for the consolidation of the number of memories required to correctly represent the pattern environment. This method is shown to be applicable to a supervised or unsupervised learning paradigm in which pairs of input and output patterns are presented sequentially to the network. The resulting learning procedure develops internal representations in an incremental or cumulative fashion, from the layer closest to the input, to the output layer.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.