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This paper addresses the problems of passivity and robust passivity for a class of stochastic switched inertial neural networks (SSINNs) with time‐varying parametric uncertainties. First, the original inertial neural networks can be converted into first‐order differential systems using a suitable variable transformation approach. Moreover, by using a proper Lyapunov–Krasovskii functional (LKF) theory, Jensen's inequality, and the state‐dependent switching (SDS) law approach, several adequate criteria are derived in terms of linear matrix inequalities (LMIs) to ensure passivity and robust passivity analysis of SSINNs with parametric uncertainties and time‐varying delays. It is demonstrated that the developed SDS law may guarantee the passivity requirements of the above‐considered system made up of all unstable subnetworks. Furthermore, the gains are obtained by solving a set of LMIs that can be easily verified by some standard numerical packages. Ultimately, two examples with numerical simulations are provided to demonstrate the efficacy and feasibility of the suggested method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of robust stability analysis for a type of uncertain stochastic switched inertial neural networks (SSINNs) with time‐varying delay is investigated. First, the original second‐order system is converted into first‐order differential equations using the variable transformation method. Next, some sufficient conditions in terms of linear matrix inequalities (LMIs) are obtained using Lyapunov‐Krasovskii functional (LKF), state‐dependent switching (SDS) method, and Jensen's integral inequality for estimating integral inequalities so that the augmented system is robust, global, and asymptotically stable in the mean square for the uncertain SSINNs with time‐varying delay. It is shown that the stability of the above considered system composed of all unstable subsystems can be achieved by using the SDS law. Finally, two numerical simulations are provided to demonstrate the effectiveness of the proposed SDS law. [ABSTRACT FROM AUTHOR]

The goal of this study is to derive a class of random impulsive fractional stochastic differential equations with finite delay that are of Caputo-type. Through certain constraints, the existence of the mild solution of the aforementioned system are acquired by Kransnoselskii's fixed point theorem. Furthermore, through Ito isometry and Gronwall's inequality, the Hyers-Ulam stability of the reckoned system is evaluated using Lipschitz condition. [ABSTRACT FROM AUTHOR]

Hydrazine complexes, M(N2H4)0.5A2 where M=Co and Cu and M(N2H4)A2 where M=Cd and Ni and Cd(N2H4)A where A = homophthalic acid(H2hp), C6H4(CH2COOH)(COOH) have been prepared by the reaction of aqueous solution of hydrazine hydrate, homophthalic acid and metal nitrate at pH = 5-7. The acid shows only mono ionic nature in the complexes by deprotonation from aliphatic carboxylic acid group. The elemental analysis of complexes, IR data shows uN-N in the range 933-987 cm-1, uC=O sym & uC=O assym of the aliphatic carboxylate respectively in the range of 1620-1481 cm-1 & 1404-1381 cm-1 respectively and uOH of COOH group around 3400 cm-1 confirm the structure of complexes. UV-Visible & VSM data substantiate the geometry of complexes. Simultaneous TG-DSC data indicate that the complexes undergo decomposition to give nano metal powder. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the approximate controllability for time-dependent impulsive neutral stochastic partial integrodifferential equations with Poisson jumps in Hilbert space. The results are obtained by using theory of resolvent operator and fixed point approach, we derive a new set of sufficient conditions for the approximate controllability of nonlinear stochastic system under the assumption that the corresponding linear system is approximately controllable. Finally, an example is provided to illustrate our results. [ABSTRACT FROM AUTHOR]