This study investigates the problem of finite-time boundedness of a class of neural networks of Caputo fractional order with time delay and uncertain terms. New sufficient conditions are established by constructing suitable Lyapunov functionals to ensure that the addressed fractional-order uncertain neural networks are finite-time stable. Criteria for finite-time boundedness of the considered fractional-order uncertain models are also achieved. The obtained results are based on a newly developed property of Caputo fractional derivatives, properties of Mittag–Leffler functions and Laplace transforms. In addition, examples are developed to manifest the usefulness of our theoretical results
The issue of robust stability for fractional-order Hopfield neural networks with parameter uncertain...
Abstract In this paper, the global robust Mittag-Leffler stability analysis is preformed for fractio...
This article investigates quasi-synchronization for a class of fractional-order delayed neural netwo...
This paper focuses on a class of delayed fractional Cohen–Grossberg neural networks with the fractio...
One of the main properties of solutions of nonlinear Caputo fractional neural networks is stability ...
One of the main properties of solutions of nonlinear Caputo fractional neural networks is stability ...
At present, the theory and application of fractional-order neural networks remain in the exploratory...
At the beginning, a class of fractional-order delayed neural networks were employed. It is known tha...
Dynamics of discrete‐time neural networks have not been well documented yet in fractional‐order case...
In this paper, the global asymptotical stability of Riemann-Liouville fractional-order neural networ...
In this paper, finite time stability analysis of fractional-order complex-valued memristive neural n...
In this paper, we study the finite-time stability and synchronization problem of a class o...
© 2016, Springer International Publishing. In this paper, we study a class of fractional-order cellu...
The lack of a conventional Lyapunov theory for fractional-order (FO) systems makes it difficult to s...
This paper focuses on investigating the finite-time projective synchronization of Caputo type fracti...
The issue of robust stability for fractional-order Hopfield neural networks with parameter uncertain...
Abstract In this paper, the global robust Mittag-Leffler stability analysis is preformed for fractio...
This article investigates quasi-synchronization for a class of fractional-order delayed neural netwo...
This paper focuses on a class of delayed fractional Cohen–Grossberg neural networks with the fractio...
One of the main properties of solutions of nonlinear Caputo fractional neural networks is stability ...
One of the main properties of solutions of nonlinear Caputo fractional neural networks is stability ...
At present, the theory and application of fractional-order neural networks remain in the exploratory...
At the beginning, a class of fractional-order delayed neural networks were employed. It is known tha...
Dynamics of discrete‐time neural networks have not been well documented yet in fractional‐order case...
In this paper, the global asymptotical stability of Riemann-Liouville fractional-order neural networ...
In this paper, finite time stability analysis of fractional-order complex-valued memristive neural n...
In this paper, we study the finite-time stability and synchronization problem of a class o...
© 2016, Springer International Publishing. In this paper, we study a class of fractional-order cellu...
The lack of a conventional Lyapunov theory for fractional-order (FO) systems makes it difficult to s...
This paper focuses on investigating the finite-time projective synchronization of Caputo type fracti...
The issue of robust stability for fractional-order Hopfield neural networks with parameter uncertain...
Abstract In this paper, the global robust Mittag-Leffler stability analysis is preformed for fractio...
This article investigates quasi-synchronization for a class of fractional-order delayed neural netwo...