Classification of prehensile EMG patterns with simplified fuzzy ARTMAP networks

Simplified fuzzy ARTMAP networks (SFAM) typically generate a large number of output neurons and require a large number of input neurons due to the input complementation. We propose a modification of SFAM, which uses activation and matching functions based on the Mahalanobis distance. This modification considerably reduces the network size and increases the efficiency in training and classification. The new network has shown an excellent performance in classification of prehensile motions based on EMG patterns.