TY - GEN
T1 - Extracted memory from temporal patterns using adaptive resonance and recurrent network
AU - Araki, Osamu
PY - 1993
Y1 - 1993
N2 - We can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction.
AB - We can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction.
UR - https://www.scopus.com/pages/publications/0027809813
M3 - Conference contribution
AN - SCOPUS:0027809813
SN - 0780314212
SN - 9780780314214
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2642
EP - 2645
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Publ by IEEE
T2 - Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
Y2 - 25 October 1993 through 29 October 1993
ER -