Neural network with semi-localized non-linear mapping of the input space
US5113483A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Jun 15, 1990 |
| Grant date | May 12, 1992 |
| Priority date | — |
| Expiry date | Jun 15, 2010 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/454
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A neural network includes an input layer comprising a plurality of input units (24) interconnected to a hidden layer with a plurality of hidden units (26) disposed therein through an interconnection matrix (28). Each of the hidden units (26) is a single output that is connected to output units (32) in an output layer through an interconnection matrix (30). Each of the interconnections between one of the hidden units (26) to one of the output units (32) has a weight associated therewith. Each of the hidden units (26) has an activation in the i'th dimension and extending across all the other dimensions in a non-localized manner in accordance with the following equation: ##EQU1## that the network learns by the Back Propagation method to vary the output weights and the parameters of the activation function .mu..sub.hi and .sigma..sub.hi.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.