受生物学启发的高层次信息融合方法

B. Rhodes
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引用次数: 3

摘要

当代态势感知问题,如异常检测和运动行为预测的自动正常学习,都是通过生物启发的处理、表示和学习方法来解决的。讨论问题和挑战,并描述我们对它们的反应。相对简单的神经原理提供了相当大的能力,提供了学习正常运动行为模型所需的能力,并利用这些模型来识别异常行为或确定感兴趣对象最有可能的未来行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biologically-inspired approaches to higher-level information fusion
Contemporary situational awareness problems such as automated normalcy learning for anomaly detection and motion behavior prediction are addressed with biologically-inspired processing, representation, and learning approaches. Issues and challenges are discussed and our responses to them described. Relatively simple neural principles provide considerable power in providing capabilities required to learn models of normal motion behavior and utilize those models to identify unusual behavior or determine the most likely future behavior of objects of interest.
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