TY - JOUR
T1 - Stable Kalman filter and neural network for the chaotic systems identification
AU - de Jesús Rubio, José
N1 - Publisher Copyright:
© 2017 The Franklin Institute
PY - 2017/11
Y1 - 2017/11
N2 - In this research, a modified Kalman filter is introduced for the adaptation of a neural network. The modified Kalman filter is an improved version of the extended Kalman filter based in the following two changes: (1) a term of the weights adaptation is modified in the modified algorithm to assure the uniform stability, convergence of the weights error, and local minimums avoidance, (2) the activation functions are used instead of the Jacobian terms in the modified algorithm to assure the boundedness of the weights error. The suggested algorithm is applied for the chaotic systems identification.
AB - In this research, a modified Kalman filter is introduced for the adaptation of a neural network. The modified Kalman filter is an improved version of the extended Kalman filter based in the following two changes: (1) a term of the weights adaptation is modified in the modified algorithm to assure the uniform stability, convergence of the weights error, and local minimums avoidance, (2) the activation functions are used instead of the Jacobian terms in the modified algorithm to assure the boundedness of the weights error. The suggested algorithm is applied for the chaotic systems identification.
UR - http://www.scopus.com/inward/record.url?scp=85029592111&partnerID=8YFLogxK
U2 - 10.1016/j.jfranklin.2017.08.038
DO - 10.1016/j.jfranklin.2017.08.038
M3 - Artículo
SN - 0016-0032
VL - 354
SP - 7444
EP - 7462
JO - Journal of the Franklin Institute
JF - Journal of the Franklin Institute
IS - 16
ER -