Identification of aerospace acoustic sources using sparse distributed associative memoryA pattern recognition system has been developed to classify five different aerospace acoustic sources. In this paper the performance of two new classifiers, an associative memory classifier and a neural network classifier, is compared to the performance of a previously designed system. Sources are classified using features calculated from the time and frequency domain. Each classifier undergoes a training period where it learns to classify sources correctly based on a set of known sources. After training the classifier is tested with unknown sources. Results show that over 96 percent of sources were identified correctly with the new associative memory classifier. The neural network classifier identified over 81 percent of the sources correctly.
Document ID
19910027882
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Scott, E. A. (Virginia Polytechnic Inst. and State Univ. Blacksburg, VA, United States)
Fuller, C. R. (Virginia Polytechnic Inst. and State Univ. Blacksburg, VA, United States)
O'Brien, W. F. (Virginia Polytechnic Institute and State University Blacksburg, United States)