Abstract
The neural-net-similar associative memory model NEUNET is able to store and reproduce patterns reading from its receptors. This article describes an enhancement of the model to associate faulty or incomplete inputs with weighted original patterns. The network is self-learning, the correction of inputs, the storage of new information and the calculation of weights which enables the model to store information with various priorities is self-organized. Priority of information is increased if it is frequently used and consequently in case of the association of unknown inputs it is more like to be associated with a high-priority original pattern. This selective behavior is the main enhancement of the model described in this contribution.
| Original language | German (Austria) |
|---|---|
| Title of host publication | 11th International Conference on Computer Aided Systems Theory - EUROCAST 2007 |
| Number of pages | 8 |
| Publication status | Published - 2007 |
Fields of science
- 102001 Artificial intelligence
- 102006 Computer supported cooperative work (CSCW)
- 102010 Database systems
- 102014 Information design
- 102015 Information systems
- 102016 IT security
- 102028 Knowledge engineering
- 102019 Machine learning
- 102022 Software development
- 102025 Distributed systems
- 502007 E-commerce
- 505002 Data protection
- 506002 E-government
- 509018 Knowledge management
- 202007 Computer integrated manufacturing (CIM)
- 102033 Data mining
- 102035 Data science
