TY - GEN
T1 - Retrieval property of associative memory with negative resistance
AU - Hayakawa, Yoshihiro
AU - Li, Hongge
AU - Nakajima, Koji
PY - 2005
Y1 - 2005
N2 - The self-connection can enlarge the memory capacity of an associative memory based on the neural network, however, the basin size of the embedded memory state shrinks. The problem of basin size is related to undesirable stable states which are spurious states. If we can destabilize these spurious states, we expect to improve the basin size. The Inverse Function Delayed(ID) model which includes the BVP model has the negative resistance on its dynamics. The negative resistance of the ID model can destabilize the equilibrium states on some regions of conventional neural network. Hence, the associative memory based on the ID model has possibilities of improving the basin size of the network which has the self-connection in order to enlarge a memory capacity. In this paper, we show the improvement of performance compared with the conventional neural network by computer simulation.
AB - The self-connection can enlarge the memory capacity of an associative memory based on the neural network, however, the basin size of the embedded memory state shrinks. The problem of basin size is related to undesirable stable states which are spurious states. If we can destabilize these spurious states, we expect to improve the basin size. The Inverse Function Delayed(ID) model which includes the BVP model has the negative resistance on its dynamics. The negative resistance of the ID model can destabilize the equilibrium states on some regions of conventional neural network. Hence, the associative memory based on the ID model has possibilities of improving the basin size of the network which has the self-connection in order to enlarge a memory capacity. In this paper, we show the improvement of performance compared with the conventional neural network by computer simulation.
UR - http://www.scopus.com/inward/record.url?scp=33745939491&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745939491&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2005.1556022
DO - 10.1109/IJCNN.2005.1556022
M3 - Conference contribution
AN - SCOPUS:33745939491
SN - 0780390482
SN - 9780780390485
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1187
EP - 1192
BT - Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
T2 - International Joint Conference on Neural Networks, IJCNN 2005
Y2 - 31 July 2005 through 4 August 2005
ER -