TY - JOUR
T1 - A novel algorithm for the modeling of complex processes
AU - De Jesús Rubio, José
AU - Lughofer, Edwin
AU - Angelov, Plamen
AU - Novoa, Juan Francisco
AU - Meda-Campaña, Jesús A.
N1 - Publisher Copyright:
© 2018 Institute of Information Theory and Automation of The Czech Academy of Sciences. All Rights Reserved.
PY - 2018
Y1 - 2018
N2 - In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples.
AB - In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples.
KW - Complex processes
KW - Kalman filter
KW - Modeling
KW - Recursive least square
UR - http://www.scopus.com/inward/record.url?scp=85044383196&partnerID=8YFLogxK
U2 - 10.14736/kyb-2018-1-0079
DO - 10.14736/kyb-2018-1-0079
M3 - Editorial
SN - 0023-5954
VL - 54
SP - 79
EP - 95
JO - Kybernetika
JF - Kybernetika
IS - 1
ER -