A. Ansari et Gp. Papavassilopoulos, A generalized learning algorithm for an automaton operating in a multiteacher environment, IEEE SYST B, 29(5), 1999, pp. 592-600
Citations number
25
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Learning algorithms for an automaton operating in a multiteacher environmen
t are considered. These algorithms are classified based on the number of ac
tions given as inputs to the environments and the number of responses (outp
uts) obtained from the environments. In this paper, we present a general cl
ass of learning algorithm for multi-input multi-output (MIMO) models. We sh
ow that the proposed learning algorithm is absolutely expedient and E-optim
al in the sense of average penalty. The proposed learning algorithm is a ge
neralization of Baba's GAE algorithm [16] and has applications in solving,
in a parallel manner, multi-objective optimization problems in which each o
bjective function is disturbed by noise [20].