An adaptive attentive learning algorithm for single-layer neural networks

An adaptive algorithm for supervised learning in single-layer neural networks is proposed. The algorithm is characterized by fast convergence and high learning accuracy. It also allows for attentive learning and control of the dynamics of single-layer neural networks. This learning algorithm is based on the Ho-Kashyap associative neural memory (ANM) recording algorithm and is suited for the learning and association of binary patterns. Simulation results for the algorithm are shown to be superior to those of the Widrow-Hoff (or least-mean-squares) adaptive learning algorithm.<>