Multilevel pattern recognition architectures for localization of mixed chemical/auditory stimuli

This paper discusses the results of our initial investigation into multilevel pattern recognition hierarchies for successfully localizing a source that simultaneously emits an auditory and a chemical stimulus. Using a single component chemical stimulus and a single frequency auditory stimulus, we are able to improve the accuracy of source localization from 82% and 83% for single mode chemical and auditory stimuli, respectively, to 96% when the two types of stimuli are evaluated in parallel to localize their source. Demonstrated improvements in localization performance for dual-mode analysis are accomplished using a multilevel pattern recognition consisting of artificial neural networks and fuzzy logic. Both preprocessing and pattern recognition algorithms are designed to be implemented in hardware for portable, compact real-time localization decisions.