An episodal neural-net computing approach to the detection and interpretation of underwater acoustic transients

Acoustic transients develop and fade away continually in ocean environments. Accordingly, detection and interpretation of these are complicated by the fact that detection and classification cannot be made on the basis of temporal snapshots alone. Interpretation of transients must rest on the processing and classification of entire episodes of such continuing signals. The authors describe experiments in the design and implementation of such an episodal associative classifier which makes concurrent use of neural network self-organization and supervised learning methodologies. This system has no difficulty classifying signals from within test data sets and is also fast, robust, adaptive, and well suited for a wide range of sequence lengths.<>