Dissipativity and passivity analysis for memristor-based neural networks with leakage and two additive time-varying delays

Fixed-time synchronization for quaternion-valued memristor-based neural networks with mixed delays

In this paper, the fixed-time synchronization (FXTSYN) of unilateral coefficients quaternion-valued memristor-based neural networks (UCQVMNNs) with mixed delays is investigated. A direct analytical approach is suggested to obtain FXTSYN of UCQVMNNs utilizing one-norm smoothness in place of decomposition. When dealing with drive-response system discontinuity issues, use the set-valued map and the differential inclusion theorem. To accomplish the control objective, innovative nonlinear controllers and the Lyapunov functions are designed. Furthermore, some criteria of FXTSYN for UCQVMNNs are given using inequality techniques and the novel FXTSYN theory. And the accurate settling time is obtained explicitly. Finally, in order to show that the obtained theoretical results are accurate, useful, and applicable, numerical simulations are presented at the conclusion.

Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components

This paper focuses on the dynamical behavior for a class of memristor-based bidirectional associative memory neural networks (BAMNNs) with additive time-varying delays in discrete-time case. The necessity of the proposed problem is to design a proper state estimator such that the dynamics of the corresponding estimation error is exponentially stable with a prescribed decay rate. By constructing an appropriate Lyapunov-Krasovskii functional (LKF) and utilizing Cauchy-Schwartz-based summation inequality, the delay-dependent sufficient conditions for the existence of the desired estimator are derived in the absence of uncertainties which are further extended to available uncertain parameters of the prescribed memristor-based BAMNNs in terms of linear matrix inequalities (LMIs). By solving the proposed LMI conditions the estimation gain matrices are obtained. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed results.

An Arcak-type state estimation design for time-delayed static neural networks with leakage term based on unified criteria

The issue of unified dissipativity-based Arcak-type state estimator design for delayed static neural networks (SNNs) with leakage term and noise distraction was considered here. An Arcak-type state observer, which is compact than the usually used Luenberger-type state estimator, is selected to implement the subject of a unified dissipativity performance of SNNs. This paper primarily concentrates on the issue of Arcak-type state estimator of delayed SNNs involving leakage delay. The first attempt is made to tackle the Arcak-type state estimator of SNNs with time delay in leakage term in this paper based on the unified criteria, by constructing a novel Lyapunov functional together with newly improved integral inequalities. As a result, a novel unified state estimation criterion is launched in the form of linear matrix inequalities (LMIs) and put forward to justify the dynamics of error system is extended dissipative with the influence of leakage term and estimator gain matrices K¯₁ and K¯₂. Finally, an interesting simulation study is ultimately explored to show the performance of the established unified dissipativity-based theoretical results, in which, comparison results are also made together with recent works as a special case.