Institute of Information Theories and Applications FOI ITHEA
In this paper, a modification for the high-order neural network (HONN) is presented. Third order
networks are considered for achieving translation, rotation and scale invariant pattern recognition. They require
however much storage and computation power for the task. The proposed modified HONN takes into account a
priori knowledge of the binary patterns that have to be learned, achieving significant gain in computation time and
memory requirements. This modification enables the efficient computation of HONNs for image fields of greater
that 100 × 100 pixels without any loss of pattern information.