A neural architecture for pattern sequence verification through inferencing
A neural architecture for pattern sequence verification through inferencing
复制标题
通过推理进行模式序列验证的神经架构
DOI：
10.1109/72.182691
复制
发表时间：
1993
影响因子：
--
通讯作者：
Scott D. G. Smith
中科院分区：
文献类型：
--
作者：
M. J. Healy;T. Caudell;Scott D. G. Smith
LAPART是一种用于逻辑推理和监督学习的神经网络结构。通过验证从先前经验推断的模式对，强调其在识别熟悉的模式序列中的用途。它由互连的自适应谐振理论（ART）网络组成。互连使LAPART能够学习从一个模式类推断另一个模式类，以形成预测序列。它基于对当前模式的识别来预测下一个模式类，并在新数据可用时测试预测。一个被证实的预测有助于验证一个熟悉的序列，而一个不被证实的预测则标志着一对新的模式。LAPART的模拟应用于验证一个假设的，已知的目标，使用一系列的传感器图像获得沿沿着预定的方法path.Application问题的解决与一个简单的策略，它示出了如何可以在一个更完整的方式来解决。其他主题，包括ART和LAPART的逻辑解释，进行了讨论。
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.