Exponential stabilization of non-autonomous delayed neural networks via Riccati equations

This paper concerns with the problem of exponential stabilization for a class of non-autonomous neural networks with mixed discrete and distributed time-varying delays. Two cases of discrete time-varying delay, namely (i) slowly time-varying; and (ii) fast time-varying, are considered. By constructing an appropriate Lyapunov-Krasovskii functional in case (i) and utilizing the Razumikhin technique in case (ii), we establish some new delay-dependent conditions for designing a memoryless state feedback controller which stabilizes the system with an exponential convergence of the resulting closed-loop system. The proposed conditions are derived through solutions of some types of Riccati differential equations. Applications to control a class of autonomous neural networks with mixed time-varying delays are also discussed in this paper. Some numerical examples are provided to illustrate the effectiveness of the obtained results.

On global dissipativity of nonautonomous neural networks with multiple proportional delays

This brief addresses the problem of global dissipativity analysis of nonautonomous neural networks with multiple proportional delays. By using a novel constructive approach based on some comparisontechniques for differential inequalities, new explicit delay-independentconditions are derived using M-matrix theory to ensure the existence ofgeneralized exponential attracting sets and the global dissipativity of thesystem. The method presented in this brief is also utilized to derive ageneralized exponential estimate for a class of Halanay-type inequalitieswith proportional delays. Finally, three numerical examples are given toillustrate the effectiveness and improvement of the obtained results.

Reachable sets bounding for generalized neural networks with interval time-varying delay and bounded disturbances

This paper deals with the problem of finding outer bound of forwards reachable sets and interbound of backwards reachable sets of generalized neural network systems with interval nondifferentiable time-varying delay and bounded disturbances. Based on constructing a suitable Lyapunov–Krasovskii functional and utilizing some improved Jensen integral-based inequalities, two sufficient conditions are derived for the existence of: (1) the smallest possible outer bound of forwards reachable sets and (2) the largest possible interbound of backwards reachable sets. These conditions are delay dependent and in the form of matrix inequalities, which therefore can be efficiently solved by using existing convex algorithms. Three numerical examples with simulation results are provided to demonstrate the effectiveness of our results.

On global exponential stability of positive neural networks with time-varying delay

This paper presents a new result on the existence, uniqueness and global exponential stability of a positive equilibrium of positiveneural networks in the presence of bounded time-varying delay. Based on some novel comparison techniques, a testable conditionis derived to ensure that all the state trajectories of the system converge exponentially to a unique positive equilibrium. Theeffectiveness of the obtained results is illustrated by a numerical example.