New results of Quick Learning for Bidirectional Associative Memory having high capacity

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.<>