A Chemical Sensor Pattern Recognition System Using a Self-Training Neural Network Classifier With Automated Outlier Detection

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 rejected and a predicted chemical analyte identification with a measure of uncertainty will be provided to the user.