TY - GEN
T1 - Automated fuzzy neural networks for nonlinear system identification
AU - Tovar, Julio César
AU - Yu, Wen
PY - 2008
Y1 - 2008
N2 - This paper discusses the identification of nonlinear dynamic system using fuzzy neural networks. It focuses on both the structure uncertainty and the parameter uncertainty which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated fuzzy neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. Firstly, an automated support vector machine is proposed within a fixed time interval for a given network construction criterion. Then the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope structure uncertainty, a hysteresis strategy is proposed to enable fuzzy neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and simulation example show the efficacy of the proposed method.
AB - This paper discusses the identification of nonlinear dynamic system using fuzzy neural networks. It focuses on both the structure uncertainty and the parameter uncertainty which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated fuzzy neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. Firstly, an automated support vector machine is proposed within a fixed time interval for a given network construction criterion. Then the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope structure uncertainty, a hysteresis strategy is proposed to enable fuzzy neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and simulation example show the efficacy of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=55249101492&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2008.4630517
DO - 10.1109/FUZZY.2008.4630517
M3 - Contribución a la conferencia
AN - SCOPUS:55249101492
SN - 9781424418190
T3 - IEEE International Conference on Fuzzy Systems
SP - 1159
EP - 1165
BT - 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
T2 - 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
Y2 - 1 June 2008 through 6 June 2008
ER -