Robust stability criteria for Takagi-Sugeno fuzzy Cohen-Grossberg neural networks of neutral type
作者： S. Muralisankar , N. Gopalakrishnan
DOI: 10.1016/J.NEUCOM.2014.04.019
关键词: Artificial neural network 、 Stability (learning theory) 、 Takagi sugeno 、 Type (model theory) 、 Linear matrix inequality 、 Mathematics 、 Fuzzy logic 、 Control theory 、 Exponential stability 、 Linear matrix
摘要: The aim of this paper is to analyze the robust stability problem Takagi-Sugeno fuzzy Cohen-Grossberg neural networks neutral type. By constructing a Lyapunov-Krasovskii functional, which contains some triple and quadruple integral terms, using vector Wirtinger-type inequality approach, delay dependent criterion obtained guarantee addressed system. These conditions are expressed in terms linear matrix inequalities that can be easily facilitated by standard numerical packages. Finally, examples given illustrate strength proposed method.
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