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Prediction interval-based ANFIS controller for nonlinear processes
conference contribution
posted on 2016-03-16, 00:00 authored by Anwar HosenAnwar Hosen, Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, L SinnottPrediction interval (PI) has been appeared as a
promising tool to quantify the uncertainties and disturbances
associated with point forecasts. Despite of its numerous applications
in prediction problems, the use of PIs in control
application is still limited. In this paper, a PI-based ANFIS
controller is proposed and designed for nonlinear systems. In
the proposed algorithm, a PI-based neural network model (PINN)
is developed to construct the PIs, and this model is used
as an online estimator of PIs for the controller. The PIs along
with other traditional inputs are used to train the inverse ANFIS
model. The developed PI-based ANFIS model is then used as
a nonlinear PI-based controller (PIC). The performance of the
proposed PIC is examined for a nonlinear numerical plant.
Simulation results revealed that the proposed PIC performance
is superior over the traditional ANFIS-based controller.
promising tool to quantify the uncertainties and disturbances
associated with point forecasts. Despite of its numerous applications
in prediction problems, the use of PIs in control
application is still limited. In this paper, a PI-based ANFIS
controller is proposed and designed for nonlinear systems. In
the proposed algorithm, a PI-based neural network model (PINN)
is developed to construct the PIs, and this model is used
as an online estimator of PIs for the controller. The PIs along
with other traditional inputs are used to train the inverse ANFIS
model. The developed PI-based ANFIS model is then used as
a nonlinear PI-based controller (PIC). The performance of the
proposed PIC is examined for a nonlinear numerical plant.
Simulation results revealed that the proposed PIC performance
is superior over the traditional ANFIS-based controller.
History
Event
International Joint Conference on Neural Networks (2016 : Vancouver, Canada)Pagination
4901 - 4907Publisher
IEEELocation
Vancouver, CanadaPlace of publication
Piscataway, N.J.Start date
2016-07-24End date
2016-07-29ISBN-13
9781509006205Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2016, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IJCNN 2016 : Proceedings of the International Joint Conference on Neural NetworksUsage metrics
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