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Bidirectional associative memory (BAM) is a kind of an artificial neuralnetwork used to memorize and retrieve heterogeneous pattern pairs. Many effortshave been made to improve BAM from the the viewpoint of computer application,and few theoretical studies have been done. We investigated the theoreticalcharacteristics of BAM using a framework of statistical-mechanical analysis. Toinvestigate the equilibrium state of BAM, we applied self-consistent signal tonoise analysis (SCSNA) and obtained a macroscopic parameter equations andrelative capacity. Moreover, to investigate not only the equilibrium state butalso the retrieval process of reaching the equilibrium state, we appliedstatistical neurodynamics to the update rule of BAM and obtained evolutionequations for the macroscopic parameters. These evolution equations areconsistent with the results of SCSNA in the equilibrium state.Comment: 13 pages, 4 figure

Analysis of Bidirectional Associative Memory using Self-consistent Signal to Noise Analysis and Statistical Neurodynamics