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Abstract

There is disclosed herein a method and apparatus for learning the characteristics of an event and for recognizing events which have been previously learned. The apparatus is comprised of forward and feedback matrices which process input signals characterizing events to be learned by comparing the convergence responses generated by the input signals at successively different levels of convergence threshold. The convergence threshold is altered until an acceptable range of convergence is reached, and then the pattern of convergence is learned. The pattern of convergence is stored for future reference. In recognition mode, the input signals are again processed at successively different convergence threshold levels until an acceptable range of convergence is reached. The pattern of convergence is then compared to the stored convergence patterns of known events to determine if the event is known. Also disclosed are methods of operating the operatus to recognize events whose input signals are cluttered by noise or which have input patterns which are incomplete. Also disclosed are a method and apparatus for learning the differences between a first event and another event with which the first event was confused during the learning mode.

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First Claim