A transient chaotic associative memory model with temporary stay function

Abstract

AbstractWhen chaotic dynamics is imparted to the neurons that compose the associative memory model, they search for stored patterns in a pattern space chaotically. However, this model has the deficiency that judgment of whether the stored pattern has been recollected or not is difficult because its behavior is always chaotic. Because all dynamics of the chaotic neurons are chaotic, chaotic transition is repeated. The transient‐chaotic associative network (TCAN) that Lee proposed changes from the state of chaos to the state of stability (nonchaos) transiently. Additionally, it has fast recollection speed, and has large memory capacity. However, the states of TCAN do not change chaotically. Based on these results, this paper proposes a transient chaotic associative memory model with a temporary stay function (TCAMMwithTSF) which has two capabilities: one is fast speed as the states of the model converge to a stored pattern, like TCAN, and the other is the ability to search the stored pattern in a pattern space chaotically, like chaotic neural networks. Finally, the characteristics and usefulness of TCAMMwithTSF are verified by a simulation study. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(2): 29–36, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21077