Optimal design of hopfield-type associative memory by adaptive stability-growth method
An adaptive stability-growth (ASG) learning algorithm is proposed for improving, as much as possible, the stability of a Hopfield-type associative memory. While the ASG algorithm can be used to determine the optimal stability instead of the well-known minimum-overlap (MO) learning algorithm with sufficiently large lower bound for MO value, it converges much more quickly than the MO algorithm in real implementation. Therefore, the proposed ASG algorithm is more suitable than the MO algorithm for real-world design of an optimal Hopfieldtype associative memory.
Publication Source (Journal or Book title)
IEICE Transactions on Information and Systems
Liang, X., & Yamaguchi, T. (1998). Optimal design of hopfield-type associative memory by adaptive stability-growth method. IEICE Transactions on Information and Systems, E81-D (1), 148-150. Retrieved from https://digitalcommons.lsu.edu/eecs_pubs/867