TY - GEN
T1 - Parameter estimation of dynamic fuzzy models from uncertain data streams
AU - Leite, Daniel
AU - Caminhas, Walmir
AU - Lemos, Andre
AU - Palhares, Reinaldo
AU - Gomide, Fernando
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Modeling of time-varying dynamic systems in real time requires the use of streams of sensor data and incremental learning algorithms. This paper introduces an incremental fuzzy modeling approach based on uncertain data streams. By uncertain data we mean data originated from unreliable sensors, imprecise perception, or description of the value of a variable represented as a fuzzy interval. An online incremental learning algorithm is used to develop the antecedent part of functional fuzzy rules and the rule base that assembles the model. A recursive least squares-like algorithm updates the parameters of a discrete state-space representation of the fuzzy rule consequents. Data uncertainty is accounted for using specificity measures of the input data. An illustrative example concerning the Lorenz attractor is given.
AB - Modeling of time-varying dynamic systems in real time requires the use of streams of sensor data and incremental learning algorithms. This paper introduces an incremental fuzzy modeling approach based on uncertain data streams. By uncertain data we mean data originated from unreliable sensors, imprecise perception, or description of the value of a variable represented as a fuzzy interval. An online incremental learning algorithm is used to develop the antecedent part of functional fuzzy rules and the rule base that assembles the model. A recursive least squares-like algorithm updates the parameters of a discrete state-space representation of the fuzzy rule consequents. Data uncertainty is accounted for using specificity measures of the input data. An illustrative example concerning the Lorenz attractor is given.
UR - http://www.scopus.com/inward/record.url?scp=84908610802&partnerID=8YFLogxK
U2 - 10.1109/NORBERT.2014.6893892
DO - 10.1109/NORBERT.2014.6893892
M3 - Conference contribution
AN - SCOPUS:84908610802
T3 - 2014 IEEE Conference on Norbert Wiener in the 21st Century: Driving Technology's Future, 21CW 2014 - Incorporating the Proceedings of the 2014 North American Fuzzy Information Processing Society Conference, NAFIPS 2014, Conference Proceedings
BT - 2014 IEEE Conference on Norbert Wiener in the 21st Century
A2 - Gibbs, Martin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Conference on Norbert Wiener in the 21st Century, 21CW 2014
Y2 - 24 June 2014 through 26 June 2014
ER -