DocumentCode
288634
Title
Multilayer network with bipolar weights
Author
Kim, Intaek
Author_Institution
Central Res. Lab., GoldStar, Seoul, South Korea
Volume
4
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
2084
Abstract
A new learning algorithm for multilayer network with bipolar weights (WNBW) is presented. The learning process includes determinations of the bipolar weights of the network and the threshold values at the activation functions in each node. The resultant network performs a perfect recall for given sets of binary input and output pairs. In addition, the network can be easily implemented using digital technology for the realization of its weights
Keywords
learning (artificial intelligence); multilayer perceptrons; transfer functions; activation functions; bipolar weights; digital technology; learning process; multilayer network; perfect recall; threshold values; Artificial neural networks; Feedforward neural networks; Feedforward systems; Logic; Multilayer perceptrons; Neural network hardware; Neural networks; Nonhomogeneous media; Sufficient conditions; Supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374535
Filename
374535
Link To Document