Abstract
AbstractMAM (Multidirectional Associative Memory) is an extended BAM (Bidirectional Associative Memory), and an associative memory model which can deal with multiple associations. If the training set has common terms, the conventional MAM often recalls the convolutional patterns. IMAM (Improved Multidirectional Associative Memory) can store them, but the structure is complex and the storage capacity is extremely small because it must use correlation matrix. In this paper, we propose a MAM with a hidden layer and its learning method. The structure is as simple as MAM and can store the training set which includes common terms. By computer simulation, we show the storage capacity is far larger than correlation learning and it is robust against noise. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(6): 1–9, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10105
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