Open-loop learning algorithm based on GSO algorithm in ANNs

Dan Xiao; Baozong Yuan; Yuping Shi

doi:10.1117/12.217529

28 August 1995 Open-loop learning algorithm based on GSO algorithm in ANNs

Dan Xiao, Baozong Yuan, Yuping Shi

Proceedings Volume 2620, International Conference on Intelligent Manufacturing; (1995) https://doi.org/10.1117/12.217529
Event: International Conference on Intelligent Manufacturing, 1995, Wuhan, China

Abstract

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.

Citation Download Citation

Dan Xiao, Baozong Yuan, and Yuping Shi "Open-loop learning algorithm based on GSO algorithm in ANNs", Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); https://doi.org/10.1117/12.217529

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