Evaluation Framework for Input Layer Preprocessing in a Radial Basis Function Neural Network
Combat Capabilities Development Command Armaments Center, Benet Laboratories Watervliet United States
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The need for immediate situational awareness updates in a military environment can be partially mitigated by employing machine learning ML at the edge of the network, where the warfighter operates. Technical challenges for edge computing, like limited power and data, require unique hardware and software implementations for viable solutions. Low power neuromorphic processors running radial basis function artificial neural networks RBFNN makes ML at the edge more practical but can introduce limitations in the data throughput. This power and data limitation can be moderated using preprocessing of the input space to magnify the most pertinent data features. This paper presents a framework for evaluating different input space paradigms in a systematic manner. Using a representative small dataset for a pyroshock event, common in the military environment, several input preprocessing paradigms are evaluated.