A Transient Chaotic Associative Memory Model with Temporary Stay Function
When chaotic dynamics is given to the neurons that compose the associative memory model, it searches for stored patterns in a pattern space chaotically. However, it has the fault that the judgment for whether the stored pattern is recollected or not is difficult because its behavior is always chaotic. As all dynamics of the chaotic neurons are chaotic, chaotic transition is repeated. One side, transient-chaotic associative network (TCAN) Lee proposed changes from the state of chaos to the state of stability (non-chaos) transiently. Additionally, it has the fast recollection speed, and has the characteristic, high 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 temporary stay function (TCAMMwithTSF) which has two abilities: one is the fast speed at the states of the model converge to a stored pattern, like TCAN, the other is the ability that it searches the stored pattern in a pattern space chaotically, like chaotic neural networks. Finally, it is verified that what character TCAMMwithTSF has and its usefulness through simulation study.