An SOM-based algorithm for optimization with dynamic weight updating.
The self-organizing map (SOM), as a kind of unsupervised neural network, has been used for both static data management and dynamic data analysis. To further exploit its search abilities, in this paper we propose an SOM-based algorithm (SOMS) for optimization problems involving both static and dynamic functions. Furthermore, a new SOM weight updating rule is proposed to enhance the learning efficiency; this may dynamically adjust the neighborhood function for the SOM in learning system parameters. As a demonstration, the proposed SOMS is applied to function optimization and also dynamic trajectory prediction, and its performance compared with that of the genetic algorithm (GA) due to the similar ways both methods conduct searches.
Chen
Yi-Yuan
YY
Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, Taiwan.
Young
Kuu-Young
KY
D000465:Algorithms; D001185:Artificial Intelligence; D007858:Learning / Q000502:physiology*; D008433:Mathematics; D016571:Neural Networks, Computer