The convergent learning of three-layer artificial neural networks for any binary-to-binary mapping

In this paper, the learning algorithm called expand-and-truncate learning (ETL) is proposed to train three-layer binary neural networks (BNN) with guaranteed convergence for any binary-to-binary mapping. The most significant contribution of this paper is the development of learning algorithm for three-layer BNN which guarantees the convergence, automatically determining a required number of neurons in the hidden layer. Furthermore, the learning speed of the proposed ETL algorithm is much faster than that of backpropagation learning algorithm in a binary field. Neurons in the proposed BNN employ a hard-limiter activation function, only integer weights and integer thresholds. Therefore, this will greatly facilitate actual hardware implementation of the proposed BNN using currently available digital VLSI technology