Householder encoding for discrete bidirectional associative memory

A novel encoding algorithm, referred to as the Householder encoding algorithm (HCA), for discrete bidirectional associative memory (BAM) is proposed. The traditional encoding algorithm suggested by B. Kosko (1988) is based on the Hebbian-type correlation method. Thus, not all training pattern pairs can be fixed points, even when the number of training pairs is small. Using the HCA, the capacity of a BAM tends to the bound of min (L_A, L_B) where L_A and L_B are the dimensions of the BAM. Simulation results show that the capacity of BAM with HCA is greatly improved compared with Kosko´s method, particularly when the input dimensions are large. Distorted inputs recall the stored pair with the best approximation when the HCA is used