Proceedings of the
The Nineteenth International Conference on Computational Intelligence and Security (CIS 2023)
December 1 – 4, 2023, Haikou, China
Stability Analysis for T-S Fuzzy Neural Network with Two Kind of Delay
1Technology College of Xiangsihu School GuangXi Minzu University, Nanning, China.
2College of Mathematics and Statistics Sciences Nanning Normal University, Nanning, China.
3College of Physics and Electronics Nanning Normal University, Nanning, China.
ABSTRACT
By studying the stability of a class of stochastic time-delay systems with uncertainty, we extend it to the stability problem of T-S fuzzy neural network systems.Summarize and improve the stability of time-delay systems based on research method,firstly, based on the form of time-delay systems, a class of T-S fuzzy stochastic neural network systems is derived, constructing a Lyapunov function, and combine Itô formulas to process functions,use addition and subtraction terms and integral inequalities to scale, and construct the symmetric matrix corresponding to this equation,then obtain the conditions for the stochastic asymptotic stability of this type of neural network system through relevant lemmas, finally, numerical examples are used to verify the conclusions obtained, ensuring their feasibility and effectiveness.
Keywords: Linear Matrix Inequalities (LMI), Uncertain stochastic time-delay systerm, Lyapunov function.
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1Technology College of Xiangsihu School GuangXi Minzu University, Nanning, China.
2College of Mathematics and Statistics Sciences Nanning Normal University, Nanning, China.
3College of Physics and Electronics Nanning Normal University, Nanning, China.
ABSTRACT
By studying the stability of a class of stochastic time-delay systems with uncertainty, we extend it to the stability problem of T-S fuzzy neural network systems.Summarize and improve the stability of time-delay systems based on research method,firstly, based on the form of time-delay systems, a class of T-S fuzzy stochastic neural network systems is derived, constructing a Lyapunov function, and combine Itô formulas to process functions,use addition and subtraction terms and integral inequalities to scale, and construct the symmetric matrix corresponding to this equation,then obtain the conditions for the stochastic asymptotic stability of this type of neural network system through relevant lemmas, finally, numerical examples are used to verify the conclusions obtained, ensuring their feasibility and effectiveness.
Keywords: Linear Matrix Inequalities (LMI), Uncertain stochastic time-delay systerm, Lyapunov function.
Download PDF