A neural architecture for pattern sequence verification through inferencing

LAPART, a neural network architecture for logical inferencing and supervised learning is discussed. Emphasizing its use in recognizing familiar sequences of patterns by verifying pattern pairs inferred from prior experience. It consists of interconnected adaptive resonance theory (ART) networks. The interconnects enable LAPART to learn to infer one pattern class from another to form a predictive sequence. It predicts a next pattern class based upon recognition of a current pattern and tests the prediction as new data become available. A confirmed prediction aids verification of a familiar sequence, and a disconfirmation flags a novel pairing of patterns. A simulation of LAPART is applied to verification of a hypothetical, known target using a sequence of sensor images obtained along a predetermined approach path. Application issues are addressed with a simple strategy, and it is shown how they could be addressed in a more complete fashion. Other topics, including a logical interpretation of ART and LAPART, are discussed