TY - JOUR
T1 - Sliding mode-based adaptive learning in dynamical-filter-weights neurons
AU - Sira-Ramírez, Hebertt
AU - Colina-Morles, Eliezer
AU - Rivas-Echeverría, Francklin
PY - 2000/5/20
Y1 - 2000/5/20
N2 - A sliding mode control strategy is proposed for the synthesis of an adaptive learning algorithm in a neuron whose weights are constituted by first-order dynamical filters with adjustable parameters, which in turn allows the representation of dynamical processes in terms of a set of such neurons. The approach is shown to exhibit robustness characteristics and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.
AB - A sliding mode control strategy is proposed for the synthesis of an adaptive learning algorithm in a neuron whose weights are constituted by first-order dynamical filters with adjustable parameters, which in turn allows the representation of dynamical processes in terms of a set of such neurons. The approach is shown to exhibit robustness characteristics and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.
UR - https://www.scopus.com/pages/publications/0034690560
U2 - 10.1080/002071700403448
DO - 10.1080/002071700403448
M3 - Article
AN - SCOPUS:0034690560
SN - 0020-7179
VL - 73
SP - 678
EP - 685
JO - International Journal of Control
JF - International Journal of Control
IS - 8
ER -