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This paper studies the problem of $H_{\infty }$ filtering for nonlinear Markov jump systems based on Takagi-Sugeno model. Firstly, we propose a hybrid event-triggered mechanism with an adjustable threshold, which not only helps to save more limited communication resources, but also excludes Zeno behavior while preserving the merits of continuous triggering. Secondly, given the threat of cyber-attacks to network security, a stochastic variable is introduced to describe the considered deception attacks in filter design. Thirdly, a less restrictive Lyapunov-Krasovskii functional (LKF), which is not required to be continuous and positive definite in a triggering interval, is constructed to establish sufficient condition on the exponential mean-square stability for the filtering error system with a weighted $H_{\infty }$ performance. Meanwhile, co-design of the desired filter and event-triggered mechanism is achieved. Finally, a tunnel diode circuit system is provided to illustrate the effectiveness and advantage of the obtained results.

In this paper, the problem of finite-time bounded control for uncertain semi-Markovian jump neural networks with mixed delays which include distributed leakage delay (DLD) and mixed time-va...

The robust stabilization problem of memristor-based coupled neural networks (MNNs) is addressed in this paper. Firstly, the fuzzy model of MNNs is obtained by considering the properties of memristor and corresponding circuit, some predictable assumptions on the boundedness and Lipschitz continuity of activation functions are formulated. Secondly, based on T-S fuzzy theory and Lyapunov-Krasovskii functional method, robust stabilization criteria are derived in form of linear matrix inequalities (LMIs). Finally a numerical example is presented to demonstrate the effectiveness of the proposed robust stabilization criteria, which well supports theoretical results.

In this paper, a dynamic delay interval method is utilized to deal with the input-to-state stability problem of discrete-time memristive neural networks (DMNNs) with two delay components. This method relaxes the restriction on upper and lower bounds of the DMNNs delay intervals, which extends the fixed interval of a time-varying delay to a dynamic one. First, a tractable model of DMNNs is obtained via using semidiscretization technique. Furthermore, by constructing several novel Lyapunov–Krasovskii functionals, free-weighting matrices and using some techniques such as Refined Jensen-based inequalities, mathematical induction, we obtain some new sufficient conditions in the form of linear matrix inequality to ensure that the considered DMNNs with two time-varying delays are input-to-state stable. The input-to-state stability criteria for the DMNNs with two time-invariant delays are also provided. Finally, two numerical examples are presented to demonstrate the effectiveness of our theoretical results.

This paper investigates the exponential and asymptotical synchronization of stochastic memristor-based neural networks (SMNNs) with time-varying delays via pinning impulsive control. A novel type of pinning impulsive controllers is introduced to synchronize the master system and slave system. Based on the physical properties of memristor, the mathematical model of SMNNs is obtained by the theories of drive-response concept, set-valued maps and stochastic differential inclusions. Then some sufficient verifiable conditions are constructed for the synchronization of SMNNs by applying the Lyapunov-Krasovskii functional (LKF) method. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, numerical examples are presented to demonstrate the effectiveness of the theoretical results.

In this paper, the problems of dissipativity and passivity analysis for memristor-based neural networks (MNNs) with both time-varying leakage delay and two additive time-varying delays are studied. By introducing an improved Lyapunov–Krasovskii functional (LKF) with triple integral terms, and combining the reciprocally convex combination technique, Wirtinger-based integral inequality with free-weighting matrices technique, some less conservative delay-dependent dissipativity and passivity criteria are obtained. The proposed criteria that depend on the upper bounds of the leakage and additive time-varying delays are given in terms of linear matrix inequalities (LMI), which can be solved by MATLAB LMI Control Toolbox. Meanwhile, the criteria for the system with a single time-varying delay are also provided. Finally, some examples are given to illustrate the effectiveness and superiority of the obtained results.

This paper investigates exponential synchronization for memristive neural networks (MNNs) with inertial and nonlinear coupling terms. Two novel hybrid mode-dependent pinning impulse control approaches are proposed, one is adaptive element-selection and pinning a part of elements in each identical node, and the other is fixed node-selection and pinning a part of identical nodes. By introducing appropriate variable substitution, the initial second-order state derivative model for MNNs is transformed into two first-order derivative equations. Then, through introducing average impulsive interval, Lyapunov–Krasovskii functional method, inequality techniques, and extended comparison principle, some corresponding exponential synchronization conditions are presented, which enrich and extend some published results. Finally, simulations are given to illustrate the exponential synchronization conditions.

With the increasing demand of millimeter-wave (MMW) communication, the performance of its frequency synthesizer has high requirements. In this paper, a multiple conversion mechanism, which used hybrid frequency synthesis technique is presented. Some key modules include linear frequency-modulated generator, agile, and low phase noise local oscillators (LOs), automatic gain control, receiver front-end circuit, and MMW LO circuits are discussed and simulated to verify correctness of the mechanism. The experimental results show that, spurious is better than −65 dBc, phase noise is no more than −80 dBc/Hz@1 kHz, and the maximum frequency-hopping time is µs.