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Shift-Invariant Associative Memory Based on Homogeneous Neural Networks Hiromi MIYAJIMA Noritaka SHIGEI Shuji YATSUKI Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E88-A No.10 pp.2600-2606 Publication Date: 2005/10/01 Online ISSN: DOI: 10.1093/ietfec/e88-a.10.2600 Print ISSN: 0916-8508 Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications) Category: Keyword: associative memory, shift-invariant, homogeneous neural networks, memory capacity, Full Text: PDF(332.4KB)>>
Summary: This paper proposes homogeneous neural networks (HNNs), in which each neuron has identical weights. HNNs can realize shift-invariant associative memory, that is, HNNs can associate not only a memorized pattern but also its shifted ones. The transition property of HNNs is analyzed by the statistical method. We show the probability that each neuron outputs correctly and the error-correcting ability. Further, we show that HNNs cannot memorize over the number,  , of patterns, where m is the number of neurons and k is the order of connections. | ||||||||||
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