Open-loop learning algorithm based on GSO algorithm in ANNs

Artificial neural networks have shown their prominence for pattern recognition, signal processing, and robot manipulation, etc., but the learning convergence procedure, generally, is long. Thus in many application fields, a more efficient learning algorithm is required. In this paper, we present an available open-loop learning algorithm for the generation of binary- to-binary mappings. This learning algorithm preserves the properties of open-loop algorithm, such as fast convergence procedure and simple design, etc. Since this open-loop algorithm is based on Gram-Schmidt Orthogonalization (GSO) algorithm, the neural network is termed as orthogonal projection binary neural networks (OPBNNs). Finally, examples are given to show the efficiency of OPBNNs.