Search

This paper deals with global synchronization problem of multiple discrete-time Markovian jump memristor-based neural networks (DTMJMNNs) with mixed mode-dependent delays via a novel event-triggered impulsive coupling control (ETICC). The parameters of the multiple DTMJMNNs and the mixed time delays (both discrete and distributed delays) switch randomly according to a Markov chain. In the ETICC strategy, the controller does not work all the time, but only works at impulse instants determined by specific events. In particular, the coupling matrix can be non-Laplacian. By using the Lyapunov stability theory, linear matrix inequalities (LMIs), and the Kronecker product, some sufficient conditions for global synchronization of multiple DTMJMNNs under the event-triggered strategy are derived. Two examples are presented to test the validity of the theoretical analysis results. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the event-based extended dissipative state estimation problem for memristor-based Markovian neural networks in the presence of hybrid time-varying delays and sensor nonlinearity. To tackle the effect caused by information latching, sudden interference and environmental variation, the Markov jump model is employed to describe the memristor-based neural network. Besides, an event-triggered scheme is introduced to economize the cost of communication. Then some novel conditions are presented, which guarantee that the augmented error system is stochastically stable with an extended dissipative performance. The existence criterion of the desired mode-dependent estimator is also obtained in terms of linear matrix inequalities. Finally, simulation results are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the issue of sampled-data stabilization for Takagi–Sugeno fuzzy memristive neural networks (FMNNs) with time-varying delay. First, the concerned FMNNs are transformed into the tractable fuzzy NNs based on the excitatory and inhibitory of memristive synaptic weights using a new convex combination technique. Meanwhile, a switched fuzzy sampled-data controller is employed for the first time to tackle stability problems related to FMNNs. Then, the novel stabilization criteria of the FMNNs are established using the fuzzy membership functions (FMFs)-dependent Lyapunov–Krasovskii functional. This sufficiently utilizes information from not only the delayed state and the actual sampling pattern but also the FMFs. Two simulation examples are presented to demonstrate the feasibility and validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new timing storage circuit based on memristors. Its ability to store and reproduce timing information in an analog manner without performing quantization can be useful for a wide range of applications. For continuous-time (CT) digital filters, the power and area costly analog delay blocks, which are usually implemented as inverter chains or their variants, can be replaced by the proposed timing storage circuits to delay CT digital signals in a more efficient way, especially for low-frequency biomedical applications that require very long tap delays. In addition, the same timing storage circuits also enable the storage of CT digital signals, extending the benefits of CT digital signal processing (DSP) to applications that require signal storage. As an example, a 15-tap CT finite impulse response (FIR) Savitzky-Golay (S-G) filter was designed with memristor-based delay blocks to smoothen electrocardiographic (ECG) signals accompanied with high-frequency noise. The simulated power consumption under a 3.3-volt supply was 6.63 \muw. [ABSTRACT FROM AUTHOR]

This article shows a focus on the positivity and stability of Cohen-Grossberg-type time-delay memristor neural networks. We start by providing the existence and unique theorem of solutions for time-delay memristor neural networks to the case of unbounded delays. It is clear to find that the discriminate criterion of the existence and uniqueness of solutions about the argumented system with unbounded delays plays an important role in other related unbounded time-delay systems. Leveraging the Lyapunov method along with memristor nonlinearity, sufficient and necessary conditions for positivity and stability of Cohen-Grossberg-type memristor neural networks under unbounded delays are gained. Our proposed criteria don’t need any strictly restrictive conditions. These theoretical results derived here will be helpful to understand the convergence performance of memristor electrical systems. [ABSTRACT FROM AUTHOR]

In this paper, the dissipativity problem of discrete-time memristive neural networks (DMNNs) with time-varying delays and stochastic perturbation is investigated. A class of logical switched functions are put forward to reflect the memristor-based switched property of connection weights, and the DMNNs are then recast into a tractable model. Based on the tractable model, the robust analysis method and Refined Jensen-based inequalities are applied to establish some sufficient conditions that ensure the (\mathcal Q,\mathcal S,\mathcal R)-\gamma -\text disspativity of DMNNs. Two numerical examples are presented to illustrate the effectiveness of the obtained results. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the exponential stability and stabilization of memristive neural networks (MNNs) with delays. First, we present some generalized double-integral inequalities, which include some existing inequalities as their special cases. Second, combining with quadratic convex combination method, these double-integral inequalities are employed to formulate a delay-dependent stability condition for MNNs with delays. Third, a state-dependent switching control law is obtained for MNNs with delays based on the proposed stability conditions. The desired feedback gain matrices are accomplished by solving a set of linear matrix inequalities. Finally, the feasibility and effectiveness of the proposed results are tested by two numerical examples. [ABSTRACT FROM AUTHOR]

This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the $\omega $ -measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results. [ABSTRACT FROM PUBLISHER]