TY - JOUR
T1 - The evolutionary learning rule for system identification
AU - Montiel, Oscar
AU - Castillo, Oscar
AU - Melin, Patricia
AU - Sepulveda, Roberto
PY - 2003
Y1 - 2003
N2 - In this paper, we are proposing an approach for integrating evolutionary computation applied to the problem of system identification in the well-known statistical signal processing theory. Here, some mathematical expressions are developed in order to justify the learning rule in the adaptive process when a breeder genetic algorithm (BGA) is used as the optimization technique. In this work, we are including an analysis of errors, energy measures, and stability.
AB - In this paper, we are proposing an approach for integrating evolutionary computation applied to the problem of system identification in the well-known statistical signal processing theory. Here, some mathematical expressions are developed in order to justify the learning rule in the adaptive process when a breeder genetic algorithm (BGA) is used as the optimization technique. In this work, we are including an analysis of errors, energy measures, and stability.
KW - ARMA
KW - Breeder genetic algorithm
KW - Learning rule
KW - Mathematical model
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=12744264891&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2003.05.005
DO - 10.1016/j.asoc.2003.05.005
M3 - Artículo
SN - 1568-4946
VL - 3
SP - 343
EP - 352
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
IS - 4
ER -