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The variable structure learning automaton network
Author(s) -
Qian Fei,
Hirata Hironori
Publication year - 1999
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/(sici)1520-6416(199910)129:1<39::aid-eej5>3.0.co;2-c
Subject(s) - flexibility (engineering) , learning automata , computer science , automaton , set (abstract data type) , continuous automaton , deterministic automaton , tracking (education) , two way deterministic finite automaton , reinforcement learning , scheme (mathematics) , action (physics) , variable (mathematics) , basis (linear algebra) , artificial intelligence , mathematics , mobile automaton , automata theory , nondeterministic finite automaton , psychology , pedagogy , mathematical analysis , statistics , physics , quantum mechanics , programming language , geometry
Learning automata select an action from a finite set of their available actions and update their strategy on the basis of response received from the random environment using what is known as a reinforcement scheme. As an environment changes, the ordering of the actions with performance criterion may vary. If a learning automaton with a fixed strategy is used in such an environment, it may become less expedient with time and even inexpedient. However, using the learning scheme that has sufficient flexibility to track the better actions makes the performance improved. In this paper, a variable structure learning automaton network with periodic random environment is proposed. The results of some numerical simulations show that our model can be used for tracking some periodic nonstationary environment for which an upper bound on the period is known. © 1999 Scripta Technica, Electr Eng Jpn, 129(1): 39–45, 1999