Probabilistic neural network for multi-criteria event detector
US7170418B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 1, 2005 |
| Grant date | Jan 30, 2007 |
| Priority date | — |
| Expiry date | Oct 14, 2025 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG08B31/00
- WIPO fieldControl
- WIPO sectorInstruments
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.