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Proceedings Paper

Open-loop learning algorithm based on GSO algorithm in ANNs
Author(s): Dan Xiao; Baozong Yuan; Yuping Shi
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Paper 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.

Paper Details

Date Published: 28 August 1995
PDF: 6 pages
Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); doi: 10.1117/12.217529
Show Author Affiliations
Dan Xiao, Northern Jiaotong Univ. (China)
Baozong Yuan, Northern Jiaotong Univ. (China)
Yuping Shi, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 2620:
International Conference on Intelligent Manufacturing
Shuzi Yang; Ji Zhou; Cheng-Gang Li, Editor(s)

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