Odor detection using pulse coupled neural networks

Based on neural structure (not physiology) observed in clinical experiments, an odor image can be constructed for analysis with a cutting-edge image processing procedure termed pulse coupled neural networks factoring (PCNNf). Enhancement of an odor image using PCNNf can significantly increase detection accuracy. Selection of the proper parameters for the implementation usually requires analysis by an expert familiar with the application targeted. Once suitable parameters have been selected, the PCNNf procedure is very robust, and can typically be used in a large number of situations similar to the original application. The purpose of this research is to advance the methodology for selecting parameters with reduced input from experts. The approach selected is use of a set of evolutionary algorithms (EA) to find improved parameter sets and to establish automated procedures for setting bounds on parameters and weight matrices for particular applications