A dynamic connectionist data compression and reconstruction (DCR) network is introduced. The network features fast learning capabilities, dynamic feedback of the output to the input, and apparent competition. It is shown that the data reconstruction procedure of the DCR network is equivalent to Wittmeyer's iterative method. Comparisons with a soft competition network, the Hebbian and anti-Hebbian network, and with principal component analysis demonstrate the superiority of the DCR network in terms of learning time since the network exhibits similar reconstruction abilities to the other networks and can make use of but does not require a slow tuning procedure. It is demonstrated that the DCR network can be added on top of other networks to improve reconstruction performance.
|Number of pages||15|
|Journal||Neural Network World|
|Publication status||Published - Jan 1 1997|
ASJC Scopus subject areas
- Hardware and Architecture
- Artificial Intelligence