A comparison of tabu algorithms for hysteresis neural networks
Toshiya Nakaguchi, Mamoru Tanaka, Kenya Jin’no · 2004
Hysteresis neural networks are one of the effective heuristic algorithms for constraint satisfaction problems. To overcome a serious defect of HNN which is called a periodic solution, several algorithms have been proposed. The paper describes a comparison between two algorithms of them based on tabu search. One is a previously proposed algorithm named dynamic time constant tabu hysteresis neural networks. Another is a novel algorithm named dynamic equilibrium point tabu hysteresis neural networks. These algorithms are estimated from their performances and implementation costs.