A Multi-Time Scale Learning Mechanism for Neuromimic Processing
Description:
Learning and representing and reasoning about temporal relations, particularly causal relations, is a deep problem in artificial intelligence (AI). Learning such representations in the real world is complicated by the fact that phenomena are subject to multiple time scale influences and may operate with a strange attractor dynamic. This dissertation proposes a new computational learning mechanism, the adaptrode, which, used in a neuromimic processing architecture may help to solve some of these…
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Date:
August 1994
Creator:
Mobus, George E. (George Edward)
Partner:
UNT Libraries