Title
Efficient learning algorithm for associative memory neural network
Document Type
Article
Publication Date
12-1-1997
Abstract
A new efficient learning algorithm of associative memory neural network is proposed, with the following characteristics: (1) it can store any given training pattern set no matter how much and what correlation among them may be; (2) the smallest domain of attraction of training patterns is maximized; (3) each domain of attraction of training patterns is guaranteed to be as large as possible; (4) the designed associative memory network is globally stable. A large number of computer experimental results confirm that this algorithm possesses more powerful storage ability and more fault-tolerance capability than existing ones.
Publication Source (Journal or Book title)
Zidonghua Xuebao/Acta Automatica Sinica
First Page
721
Last Page
727
Recommended Citation
Liang, X., Wu, L., & Yu, J. (1997). Efficient learning algorithm for associative memory neural network. Zidonghua Xuebao/Acta Automatica Sinica, 23 (5), 721-727. Retrieved from https://repository.lsu.edu/eecs_pubs/869