Probabilistic neural network for multi-criteria fire detector
First Claim
1. A multi-criteria event detection system comprising:
- a plurality of sensors, wherein each said 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 of said outputs of said plurality of sensors, said processor including a probabilistic neural network for processing said outputs, and wherein said probabilistic neural network comprises a nonlinear, non-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, and wherein said plurality of data sets includes;
a baseline, non-event, first data set;
a second, event data set; and
a third, nuisance data set;
wherein said algorithm provides a decisional output indicative of the presence of the event based on recognizing and discriminating between said data sets and whether said outputs suffice to substantially indicate the presence of the event as opposed to the non-event or a nuisance situation.
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Abstract
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.
178 Citations
11 Claims
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1. A multi-criteria event detection system comprising:
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a plurality of sensors, wherein each said 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 of said outputs of said plurality of sensors, said processor including a probabilistic neural network for processing said outputs, and wherein said probabilistic neural network comprises a nonlinear, non-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, and wherein said plurality of data sets includes;
a baseline, non-event, first data set;
a second, event data set; and
a third, nuisance data set;
wherein said algorithm provides a decisional output indicative of the presence of the event based on recognizing and discriminating between said data sets and whether said outputs suffice to substantially indicate the presence of the event as opposed to the non-event or a nuisance situation. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for detecting the presence of an event, comprising:
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establishing a plurality of data sets, said data sets including;
a baseline, non-event, first data set;
a second, event data set; and
a third nuisance data set;
training each of said data sets to respond to an input and provide a representative output;
sensing a plurality of signatures;
encoding each of said plurality of signatures in a numerical output representative of a point or location in a multidimensional space;
inputting each said numerical output to a probabilistic neural network, said network defining a probability density function for each said data set based on said training set data and an optimized kernel width parameter; and
correlating said numerical outputs to a location in said multidimensional space to determine the presence or absence of the event at said location. - View Dependent Claims (8, 9, 10, 11)
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Specification