Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
This paper investigates the synchronization problem of delayed Markovian coupled neural networks with random coupling strengths and partial information on transition probabilities. In this coupled neural network model, each transition rate is only known partial information, and the coupling strengths are characterized by mutually independent random variables. By constructing a new augmented Lyapunov-Krasovskii...
This paper investigates the problem of mean square exponential stability for uncertain stochastic interval type-2 (IT2) fuzzy neural networks with multiple time-varying delays. First, IT2 fuzzy neural network is introduced, which takes time delays and parameter uncertainties into account. Compared with the existing results, our model is more applicable since time delays and parameter uncertainties...
In this paper, we are concerned with the problem of reliable H∞ control for state estimation of T-S fussy delayed neural networks. The main objective of this paper is to design a desirable reliable controller such that the zero solution of the error system is globally asymptotically stable with a guaranteed H∞ performance index γ. Based on the convex combination technique and the secondary delay-partitioning...
This study is concerned with the issue of H∞ state estimation for static neural networks (SNNs) with time-varying delays. Firstly, we employ a new alterable terminal method (ATM), which is proposed by applying convex combination method and introducing a tunable parameter. Secondly, on the basis of the ATM, we choose an augmented Lyapunov-Krasovskii functional (LKF), where the integral interval connected...
In this paper, the problem of delay-dependent H∞ state estimation for static neural networks with time-varying delay is investigated. By introducing a new double-integral inequality and constructing a more general Lyapunov-Krasovskii functional (LKF) including a triple integral term, an improved delay-dependent design condition is established so that the error system is globally exponentially stable...
This paper studies the problem of H∞ state estimation for neural networks with mixed time-varying delays. Firstly, based on a newly augmented Lyapunov-Krasovskii functional (LKF), novel delay-dependent conditions are obtained such that the error system is globally asymptotically stable with H∞ performance index γ. Secondly, less conservative stable results are established by employing some effective...
This paper is focused on the problem of delay-dependent stabilization for neural networks (NNs) with discrete and distributed time-varying delays. The main objective of this work is to design a H∞ control law to ensure the asymptotical stability of the closed-loop system. Besides, the less conservative stability criterion is derived in terms of linear matrix inequalities (LMIs) by constructing an...
This study is focused on the problem of asymptotic stability for a class of Lur'e dynamical system with mixed time-varying delays and nonlinearity. Firstly, by constructing a new appropriate Lyapunov-Krasovkii functional, less conservative delay-dependent stability criteria are derived by using some integral inequalities. Besides, by dividing the mixed delays into many a nonuniformly subintervals...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.