Importance

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Abstract

There is disclosed herein a recognize only embodiment of a recognition matrix comprised of a forward matrix and a reverse matrix each having a plurality of contacts which cause convergence responses on target lines when an input signal is received by said contact. Learning is performed by changing the characteristics of the contacts to alter the convergence responses they cause in accordance with a learning rule involving the comparison of total convergence response on each target line to a convergence threshold. The contacts are not programmed ad hoc in the field as events are individually learned. Instead each contact is programmed permanently by the user for a class of events which is fixed and which can never change. The user typically performs the learning on a computer simulator for all the events which a particular system is to be used to recognize. The patterns of convergence responses and contact structure characteristics which cause these convergence responses for the class of events as a whole are then examined and optimized for maximum recognition power and minimum confusion. This pattern of convergence responses or contact characteristics is then permanently programmed in the contacts of the forward and reverse matrices. A no-confusion embodiment is also disclosed whereby an array oif recognition machines are each programmed to recognize only one event, and all are coupled in parallel to an input bus carrying the signals characterizing the event to be recognized. The outputs are or'ed together.

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First Claim