Lag quasi-synchronization for memristive neural networks with switching jumps mismatch

Abstract

This paper is concerned with the lag quasisynchronization for memristive neural networks (MNNs) with switching jumps mismatch. The inherent characteristic of MNNs is fully taken into account. Based on Lyapunov-Krasovskii functional and differential inclusions theory, intermittent control approach is utilized to realize the exponential lag quasi-synchronization of the considered model. The error level is closely related to the switching jumps. In addition, a simple design procedure of controller is presented to ensure that the synchronization error between the master system and the slave system converges to a predetermined level. Two numerical examples are offered to show the effectiveness of the proposed method.

State estimation for complex-valued memristive neural networks with time-varying delays

“…Then a LMI-based result with lower computational burden is obtained, which considers the sign difference of the memristive weights and overcomes the shortcomings of the results based on M-matrix and algebraic inequality. The gain matrix K can be determined easily by solving the certain matrix inequalities (27). Besides, if system (1) is reduced to real-valued memristive neural networks, a similar result can also be derived.…”

“…By means of Lemma 2, complex-valued LMIs (28) can be transformed into real-valued ones described in (27). Obviously, (27) can guarantee that (11) holds.…”

“…Moreover, it is also shown to be similar to the synapses in the human brain. Based on these features, the memristive neural networks model established by replacing a resistor with a memristor has attracted more and more attention, and various dynamical behaviors of this model have been investigated, see [19][20][21][22][23][24][25][26][27] and the references therein. However, when it comes to the state estimation problem, only [28,29] studied the related content.…”

Hybrid Control Scheme for Projective Lag Synchronization of Riemann–Liouville Sense Fractional Order Memristive BAM NeuralNetworks with Mixed Delays

“…In [50,54,55], the authors discussed lag synchronization of integer and fractional order memristive neural networks with switching jump mismatch, based on different control approaches including a state feedback control, period intermittent control. In [56,57], authors proposed the projective synchronization with (or without) memristive neural networks via hybrid control schemes in the Caputo sense.…”

Exponential Lag Synchronization of Memristive Neural Networks with Reaction Diffusion Terms via Neural Activation Function Control and Fuzzy Model

“…Based on the above discussions, we can know this T-S fuzzy system has complex dynamical behaviors. researches represented in [40] have also exhibited the T-S fuzzy model is a better available paradigm for image processing problems.…”