TY - JOUR
AU - Lee, Gyu
AB - This study focuses on the finite-time extended dissipativity of delayed Takagi–Sugeno (T–S) fuzzy neural networks (NNs). Based on the concept of extended dissipativity, this paper solves the H , L  L , passive, and ðQ; S; RÞ-dissipativity 1 2 1 performance in a unified framework. Using the free-matrix-based double integral inequality and an extended Wirtinger inequality in the Lyapunov–Krasovskii functional, sufficient conditions are derived to guarantee that the considered NNs are finite-time bounded, whereupon the finite-time extended dissipativity criteria for delayed T–S fuzzy NNs are con- structed. The derived conditions guarantee the extended dissipativity and stability of the NNs. Three numerical examples are given to demonstrate the reduced conservatism and the effectiveness of the obtained results. Keywords Extended dissipativity  Extended Wirtinger inequality  Finite-time bounded  Free-weighting matrix Lyapunov–Krasovskii functional  Takagi–Sugeno fuzzy neural networks 1 Introduction delayed NNs have received considerable attention, and many interesting results have been proposed [5, 40]. Neural networks (NNs) constitute an effective information The T–S fuzzy model [25] is essentially a multi-model processing and modeling paradigm that conveniently approach in which some linear models are blended into an addresses many practical problems such as real-time overall single model using nonlinear membership 
TI - Finite-time extended dissipativity of delayed Takagi–Sugeno fuzzy neural networks using a free-matrix-based double integral inequality
JF - Neural Computing and Applications
DO - 10.1007/s00521-019-04348-w
DA - 2019-07-15
UR - https://www.deepdyve.com/lp/springer-journals/finite-time-extended-dissipativity-of-delayed-takagi-sugeno-fuzzy-2PGFc4HkBG
SP - 1
EP - 12
VL - OnlineFirst
IS - 
DP - DeepDyve
ER -