Abstract
Neural logic networks are generalized to cater to logical systems where the validity of rules and facts changes with time. To construct a temporal network, the validity of rules and facts is collected at a selection of time instances to determine the connecting weights of the respective instances. The weight of the temporal network is then defined as functions that would produce the known values when the proper time is substituted. Three theorems on temporal pattern recognition are proved. >
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