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    Probabilistic neural network for multi-criteria fire detector
    1.
    发明申请
    Probabilistic neural network for multi-criteria fire detector 失效
    用于多标准火灾探测器的概率神经网络

    公开(公告)号：US20060006997A1

    公开(公告)日：2006-01-12

    申请号：US11217852

    申请日：2005-09-01

    申请人： Susan Rose-Pehrsson ,  Ronald Schaffer ,  Daniel Gottuk ,  Sean Hart ,  Mark Hammond

    发明人： Susan Rose-Pehrsson ,  Ronald Schaffer ,  Daniel Gottuk ,  Sean Hart ,  Mark Hammond

    IPC分类号： G08B19/00 ,  G08B17/10

    CPC分类号： G08B29/183 ,  G08B17/00 ,  G08B31/00

    摘要： A multi-criteria event detection system, comprising a plurality of sensors, wherein each sensor is capable of detecting a signature characteristic of a presence of an event and providing an output indicating the same. A processor for receiving each output of the plurality of sensors is also employed. The processor includes a probabilistic neural network for processing the sensor outputs. The probabilistic neural network comprises a nonlinear, nor-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter. The plurality of data sets includes a baseline, non-event, first data set; a second, event data set; and a third, nuisance data set. The algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discrimination between said data sets, and whether the outputs suffice to substantially indicate the presence of an event, as opposed to a non-event or nuisance situation.

    摘要翻译： 一种多标准事件检测系统，包括多个传感器，其中每个传感器能够检测事件存在的签名特征并提供指示该事件的输出。 还采用用于接收多个传感器的每个输出的处理器。 处理器包括用于处理传感器输出的概率神经网络。 概率神经网络包括非线性，非参数模式识别算法，其通过为多个基于训练集数据和优化的内核宽度参数的多个数据集定义概率密度函数来操作。 多个数据集包括基线，非事件，第一数据集; 第二个事件数据集; 和第三个烦人的数据集。 该算法提供了指示基于所述数据集之间的识别和区分的火灾存在的决定性输出，以及与非事件或烦扰情况相反，输出是否足以基本上指示事件的存在。