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Evaluation framework for input layer preprocessing in a radial basis function neural network

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. The correlation coefficient across the dataset, between the number of neurons and inference accuracy for the RBFNN has a p-value of 1 x 10-7.