Adaptive selection of the optimal order of linear regression models using learning automata

Abstract

This paper concerns the adaptive selection of the optimal order of linear regression models using a variable-structure stochastic learning automaton. The Alaike criterion is derived for stationary and non-stationary cases, and it is shown that the optimal order minimizes a loss function corresponding to the evaluation of this criterion. The order of the regression model belongs to a finite set. Each order value is associated with an action of the automaton. The Bush-Mosteller reinforcement scheme with normalized automaton input is used to adjust the probability distribution. Simulation results illustrate the feasibility and performance of this model order selection approach

Additional information

Notes on contributors

A. S. POZNYAK

Fax: + (52)5 7477089; e-mail: [email protected]

K. NAJIM

Tel: +(33) 62 25 23 69; Fax: +(33) 62 25 23 18

E. IKONEN

Tel: +(33) 62 25 23 69; Fax: +(33) 62 25 23 18