Open AccessAdaptive continuous action‐set learning automata scheme
Author(s) -
Guo Ying,
Li Shenghong,
Fan Bo
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.4014
Subject(s) - learning automata , automaton , scheme (mathematics) , computer science , set (abstract data type) , process (computing) , noise (video) , core (optical fiber) , action (physics) , artificial intelligence , adaptive learning , function (biology) , algorithm , mathematics , mathematical analysis , telecommunications , physics , quantum mechanics , evolutionary biology , image (mathematics) , biology , programming language , operating system
Learning automata (LA) has been widely encountered in signal processing for images, speeches, videos and so on. One special type of LA, i.e. the CALA (continuous action‐set LA) issue is handled. It first fills the existing gap aimed at the classic Beigy's algorithm, based on which a novel adaptive CALA (ACALA) algorithm is proposed. The proposed ACALA algorithm includes a sampling process and an iterating process, where the learning parameters are adaptively adjusted, not only between the two stages but also inside the core stage. Experiments with regard to noisy function optimisation reveal its outperformance, in accordance with the general evaluation system built. Specifically, the final result of the proposed algorithm could always judge as the global optimum even in noise‐added scenarios, regardless of the initial parameters.