Title :
New results of Quick Learning for Bidirectional Associative Memory having high capacity
Author :
Hattori, Motonobu ; Hagiwara, Masafumi ; Nakagawa, Masao
Author_Institution :
Dept. of Electr. Eng., Keio Univ., Yokohama, Japan
fDate :
27 Jun-2 Jul 1994
Abstract :
Several important characteristics of Quick Learning for Bidirectional Associative Memory (QLBAM) are introduced. QLBAM uses two stage learning. In the first stage, the BAM is trained by Hebbian learning and then by Pseudo-Relaxation Learning Algorithm for BAM (PRLAB). The following features of the QLBAM are made clear: it is insensitive to correlation of training pairs; it is robust for noisy inputs; the minimum absolute value of net inputs indexes a noise margin; the memory capacity is greatly improved: the maximum capacity in our simulation is about 2.2N
Keywords :
Hebbian learning; content-addressable storage; learning (artificial intelligence); neural nets; Hebbian learning; PRLAB; Pseudo-Relaxation Learning Algorithm; QLBAM; Quick Learning for Bidirectional Associative Memory; correlation; high capacity; maximum capacity; memory capacity; minimum absolute value; net inputs; noise margin; noisy inputs; robust; simulation; training pairs; two stage learning; Associative memory; Biological neural networks; Brain modeling; Hebbian theory; Magnesium compounds; Neurons; Noise reduction; Noise robustness; Relaxation methods; Vectors;
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.1994.374333
