No abstract

In this paper, the asymptotic stability of solutions is investigated for a class of nonlinear fractional neutral neural networks with time-dependent delays which are unbounded. By constructing the appropriate Lyapunov functional, sufficient conditions for asymptotic stability of neural networks are obtained with the help of LMI. An example is presented by using the LMI Toolbox to demonstrate the effectiveness of the obtained results.

We discuss the asymptotic stability of autonomous nonlinear fractional order systems, in which the state equations contain integer derivative and fractional order. We use the Lyapunov's second method to derive some sufficient conditions to ensure asymptotic stability of nonlinear fractional order differential equations. We also give two examples in order to consolidate the obtained results.

In this paper, we have delivered asymptotic stability results for solutions to non-autonomous nonlinear neutral systems. The acquired stability results are independent of the delays, and the delays are also both time-variable and unbounded. Additionally, the results were described as a convex optimization problem, and an example were used to examine the results' feasibility and efficacy.

In this paper, we investigate the boundedness and uniformly asymtotically stability of the solutions to a certain third order non-autonomous differential equations with bounded delay. By constructing a Lyapunov functional, sufficient conditions for the stability and boundedness of solutions for equations considered are obtained. We used an example to demonstrate the feasibility of our results. The results obtain essentially improve, include and complement the results in the literature.