Open-loop learning algorithm based on GSO algorithm in ANNs
Dan Xiao, Baozong Yuan, Yuping Shi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
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.