Dissipativity analysis for discrete-time fuzzy neural networks with leakage and time-varying delays
Z Ma, G Sun, D Liu, X Xing - Neurocomputing, 2016 - Elsevier
Z Ma, G Sun, D Liu, X Xing
Neurocomputing, 2016•ElsevierThis paper investigates the problem of dissipativity analysis for discrete-time fuzzy neural
network with parameter uncertainties based on interval type-2 (IT2) fuzzy model. The
parameter uncertainties are handled via the lower and upper membership functions. The
original sufficient conditions are presented by a set of linear matrix inequalities (LMIs) to
guarantee the dissipativity of the resulting system. The main contribution of this paper is that
the discrete-time form of the IT2 T–S fuzzy neural network with leakage and time-varying …
network with parameter uncertainties based on interval type-2 (IT2) fuzzy model. The
parameter uncertainties are handled via the lower and upper membership functions. The
original sufficient conditions are presented by a set of linear matrix inequalities (LMIs) to
guarantee the dissipativity of the resulting system. The main contribution of this paper is that
the discrete-time form of the IT2 T–S fuzzy neural network with leakage and time-varying …
Abstract
This paper investigates the problem of dissipativity analysis for discrete-time fuzzy neural network with parameter uncertainties based on interval type-2 (IT2) fuzzy model. The parameter uncertainties are handled via the lower and upper membership functions. The original sufficient conditions are presented by a set of linear matrix inequalities (LMIs) to guarantee the dissipativity of the resulting system. The main contribution of this paper is that the discrete-time form of the IT2 T–S fuzzy neural network with leakage and time-varying delays is first proposed. Finally, a numerical example is provided to testify the effectiveness of the proposed results.
Elsevier
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