@inproceedings{302109cc42a4415eb578984207e35c71,
title = "Adaptive predictor for control of nonlinear systems based on neurofuzzy models",
abstract = "This paper proposes a general nonlinear adaptive predictor using a class of neurofuzzy models. The obtained predictor may be seen as a linear predictor network consisting of a global linear predictor and several local linear predictors with interpolation. It has distinctive features as well as good prediction ability: its parameters have explicit meanings useful for initial values setting: it may be transformed into a form linear for the variables synthesized in control systems, making deriving a control law straightforward.",
keywords = "AR-MAX modeling, Nonlinear system, adaptive prediction, neurofuzzy model, nonlinear control",
author = "J. Hu and K. Hirasawa and K. Kumamaru",
year = "2015",
month = mar,
day = "24",
doi = "10.23919/ecc.1999.7100016",
language = "English",
series = "European Control Conference, ECC 1999 - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4337--4342",
booktitle = "European Control Conference, ECC 1999 - Conference Proceedings",
note = "1999 European Control Conference, ECC 1999 ; Conference date: 31-08-1999 Through 03-09-1999",
}