Title :
Multilayer network with bipolar weights
Author_Institution :
Central Res. Lab., GoldStar, Seoul, South Korea
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374535
