An ultimate goal of placing fire detection systems in buildings and structures is to allow for the rapid detection of fire and accurate faster than real time prediction of ensuing fire behavior so that relevant information can be delivered to the appropriate stakeholders. In the near-term, development of detection systems with decreased detection time, better discrimination against nuisance and false alarms, and real-time monitoring of the fire state is a critical interim step. Building comfort and efficiency systems are increasingly incorporating a greater quantity of sensors and these sensors are installed at a greater density than any fire sensor with the exception of the sprinkler. While currently used primarily for building management purposes, the application of these, or similar types of building sensors, for rapid fire detection, fire state determination, and fire forecasting offers great potential. This paper discusses the potential benefits of the application of Hierarchical Temporal Memory algorithms for fire state determination in a continuous learning environment based on its application to a series of live fire experiments.

中文翻译：

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用于火灾状态确定的分层时间记忆连续学习算法

在建筑物和结构中放置火灾探测系统的最终目标是允许快速探测火灾并比实时预测随后的火灾行为更准确，以便将相关信息传递给适当的利益相关者。在近期内，开发具有缩短检测时间、更好地识别干扰和误报以及实时监控火灾状态的检测系统是关键的过渡步骤。建筑舒适度和效率系统越来越多地包含更多数量的传感器，并且这些传感器的安装密度比除洒水器外的任何火灾传感器都大。虽然目前主要用于建筑管理目的，但这些或类似类型的建筑传感器的应用，用于快速火灾探测，火灾状态确定和火灾预测提供了巨大的潜力。本文基于分层时间记忆算法在一系列实弹实验中的应用，讨论了在连续学习环境中应用分层时间记忆算法进行火灾状态确定的潜在好处。