Chemical sensor pattern recognition system and method using a self-training neural network classifier with automated outlier detection
US6289328A · kind A · utility
Assignee
Inventor
Key dates
| Filing date | Apr 17, 1998 |
| Grant date | Sep 11, 2001 |
| Priority date | — |
| Expiry date | Apr 17, 2018 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/2433
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A device and method for a pattern recognition system using a self-training neural network classifier with automated outlier detection for use in chemical sensor array systems. The pattern recognition system uses a Probabilistic Neural Network (PNN) training computer system to develop automated classification algorithms for field-portable chemical sensor array systems. The PNN training computer system uses a pattern extraction unit to determine pattern vectors for chemical analytes. These pattern vectors form the initial hidden layer of the PNN. The hidden layer of the PNN is reduced in size by a learning vector quantization (LVQ) classifier unit. The hidden layer neurons are further reduced in number by checking them against the pattern vectors and further eliminating dead neurons using a dead neuron elimination device. Using the remaining neurons in the hidden layer of the PNN, a global, .sigma. value is calculated and a threshold rejection value is determined. The hidden layer, .sigma. value and the threshold value are then downloaded into a PNN module for use in a chemical sensor field unit. Based on the threshold value, outliers seen in the real world environment may be rejecte…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.