Synchronization of stochastic perturbed chaotic neural networks with mixed delays

In this paper, we study the synchronization problem of a class of chaotic neural networks with time-varying delays and unbounded distributed delays under stochastic perturbations. By using Lyapunov-Krasovskii functional, drive-response concept, output coupling with delay feedback and linear matrix inequality (LMI) approach, we obtain some sufficient conditions in terms of LMIs ensuring the exponential synchronization of the addressed neural networks. The feedback controllers can be easily obtained by solving the derived LMIs. Moreover, the main results are generalizations of some recent results reported in the literature. A numerical example is also provided to demonstrate the effectiveness and applicability of the obtained results.     

Synchronization of biological neural network systems with stochastic perturbations and time delays

With advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in the understanding of the molecular properties of anesthetic agents. However, despite these advances, we still do not understand how anesthetic agents affect the properties of neurons that translate into the induction of general anesthesia at the macroscopic level. There is extensive experimental verification that collections of neurons may function as oscillators and the synchronization of oscillators may play a key role in the transmission of information within the central nervous system. This may be particularly relevant to understand the mechanism of action for general anesthesia. In this paper, we develop a stochastic synaptic drive firing rate model for an excitatory and inhibitory cortical neuronal network in the face of system time delays and stochastic input disturbances. In addition, we provide sufficient conditions for global asymptotic and exponential mean-square synchronization for this model.     

Synchronization of chaotic systems with unknown parameters using adaptive passivity-based control

This paper investigates the problem of complete synchronization of chaotic systems with unknown parameters. An adaptive control scheme based on a feedback passivity approach is proposed. The convergence of the synchronization error is guaranteed. The unified chaotic and hyperchaotic Lü systems are taken as illustrative examples. The feasibility and effectiveness of the proposed scheme are demonstrated through numerical simulations.     

Synchronization of complex networks with hybrid delays based on event intermittent control

This paper studies the exponential synchronization problem for complex networks with hybrid delays via means of the event intermittent control (EIC) strategy. Compared with the traditional aperiodically intermittent control, the control instants and the rest instants are generated when the events occur, which is more in accordance with the actual situation. An modified lemma related to delays is derived without predesigning intermittent instants. Furthermore, some synchronization criteria with less conservatism are established in terms of linear matrix inequalities (LMIs). Meanwhile, it is also shown that Zeno behavior is excluded. Finally, simulations of a numerical example are given to verify the effectiveness of the proposed EIC strategy.     

Synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay

This paper proposes new delay-dependent synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay. By constructing a suitable Lyapunov–Krasovskii's functional and utilizing Finsler's lemma, novel synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by using the LMI toolbox in MATLAB. Three numerical examples are given to illustrate the effectiveness of the proposed methods.     

Synchronization of fractional-order spatiotemporal complex-valued neural networks in finite-time interval and its application

This paper focuses on the synchronization of fractional-order complex-valued neural networks (FOCVNNs) with reaction–diffusion terms in finite-time interval. Different from the existing complex-valued neural networks (CVNNs), the reaction–diffusion phenomena and fractional derivative are first considered into the system, meanwhile, the parameter switching (the system parameters will switch with the state) is considered, which makes the presented model more comprehensive. By choosing an appropriate Lyapunov function, the driver and response systems achieve Mittag-Leffler synchronization under a suitable controller. In addition, based on the fractional calculus theorem and the basic inequality methods, a criterion of synchronization for the error system in finite-time interval is derived and the upper bound of the corresponding finite synchronization time can be obtained. Finally, two examples are provided, one is a numerical example to explain the effectiveness of the main results, and the other shows that the results of this paper can be applied to image encryption for any size with high-security coefficient.     

Set stability and synchronization of logical networks with probabilistic time delays

Using the algebraic state space representation (ASSR) method, this paper investigates the set stability and synchronization of Boolean networks with probabilistic time delays (PTDs). Firstly, an equivalent stochastic system is established for the Boolean network with PTDs by using the ASSR method. Secondly, based on the probabilistic state transition matrix of equivalent stochastic system, a necessary and sufficient condition is proposed for the set stability of Boolean networks with PTDs. Thirdly, as an application of set stability, the synchronization of coupled Boolean networks with PTDs is studied, and a necessary and sufficient condition is presented. Finally, an illustrative example is given to demonstrate the effectiveness of the obtained new results.     

Finite-time lag synchronization of master-slave complex dynamical networks with unknown signal propagation delays

This paper studies the finite-time lag synchronization issue of master-slave complex networks with unknown signal propagation delays by the linear and adaptive error state feedback approaches. The unknown signal propagation delays are fully considered and estimated by adaptive laws. By designing new Lyapunov functional and discontinuous feedback controllers, which involves the estimated error rather than the general synchronization error, sufficient conditions are derived to ensure lag synchronization of the networks within a setting time. It is interesting to discover that the setting time is related to initial values of both the estimated error and the estimated unknown signal propagation delays. Finally, two numerical examples are given to illustrate the effectiveness and correctness of the proposed finite-time lag synchronization criteria.     

Global neuro-adaptive control of nonstrict-feedback systems with unknown control directions and multiple time delays

Issues of control of nonstrict-feedback systems with unknown control directions and multiple time delays are investigated. The proposed design consists of three major parts, a nominal minimal-learning-parameter (MLP) based adaptive neural controller, a supervisory robust controller for pulling back the escaped transients, and the dynamic surface control (DSC) for solving the explosion of complexity and algebraic-loop problems simultaneously. Meanwhile, the Nussbaum gain function (NGF) and the Lyapunov–Krasovskii functional (LKF) are included for handling the unknown control directions and the time delays, respectively. In particular, global instead of the semi-global tracking stability is achieved. Simulation results are provided to show the effectiveness of the proposed approach.     

Pinning synchronization of fractional-order memristor-based neural networks with multiple time-varying delays via static or dynamic coupling

This paper investigates global asymptotical synchronization between fractional-order memristor-based neural networks (FMNNs) with multiple time-varying delays (MTDs) by pinning control. Two classes of coupling manners, static manner and dynamic manner, are introduced into the pinning controller respectively. For the case of static coupling, to make the controller exclude fraction, 1-norm Lyapunov function and fractional Halanay inequality in MTDs case are utilized for synthesis of controller and convergence analysis of synchronization error. For the case of dynamic coupling, a fractional differential inequality is proved and discussed in an elaborate way, and then global asymptotical synchronization is analyzed by means of Lyapunov-like function and the newly-proved inequality. Lastly, numerical simulations are carried out to show the practicability of the pinning controllers and the feasibility of the obtained synchronization criteria.     

Containment control of fractional-order multi-agent systems with time-varying delays

In this paper, containment control problems of networked fractional-order multi-agent systems with time-varying delays are studied. The normalized directed graphs are employed to characterize the communication topologies. Two sampled-data based containment control protocols are proposed, which can overcome the time-varying delays and switching topologies. It is interestingly found that the decays of the closed-loop systems correspond to the Mittag-Leffler function and its approximation, which are the extensions of the exponential function and its approximation, respectively. Based on the algebraic graph theory, the properties of row-stochastic matrix, and the relation between the topologies and the matrices, some conditions for containment control are established. For the fixed topology, a necessary and sufficient condition is obtained; and for the switching topology, a sufficient condition is provided. Finally, the theoretical results are illustrated by several numerical simulations.     

Lag synchronization for fractional-order memristive neural networks with time delay via switching jumps mismatch

This paper studies drive-response synchronization in fractional-order memristive neural networks (FMNNs) with switching jumps mismatch. A comparison theorem for fractional-order systems with variable order is provided first. Theories of fractional order Filippov differential inclusions are used to treat FMNNs because the parameters of FMNNs are state dependent and the FMNNs has discontinuous right hand sides. Based on Laplace transform and linear feedback control, some lag quasi-synchronization conditions are obtained with variable order α: 0?<?α?<?1 and 1?<?α?<?2. The error level is estimated and the larger synchronization regain is discussed. Finally, two numerical examples are presented to illustrate the effectiveness of our proposed theorems.     

Synchronization of discrete-time complex dynamical networks with interval time-varying delays via non-fragile controller with randomly occurring perturbation

This paper addresses synchronization problem for discrete-time complex dynamical networks with interval time-varying delays. In order to achieve the synchronization, a feedback controller subjected to randomly occurring perturbations will be considered. The randomly occurring perturbations are assumed to belong to the Binomial sequence. By constructing a suitable Lyapunov–Krasovskii functional, and utilizing reciprocally convex approach and Finsler?s lemma, the synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. The networks are represented by the use of Kronecker product technique. The effectiveness of the proposed methods will be verified via numerical examples.     

Further analysis of global robust stability of neural networks with multiple time delays

This paper deals with the problem of the global robust asymptotic stability of the class of dynamical neural networks with multiple time delays. We propose a new alternative sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point under parameter uncertainties of the neural system. We first prove the existence and uniqueness of the equilibrium point by using the Homomorphic mapping theorem. Then, by employing a new Lyapunov functional, the Lyapunov stability theorem is used to establish the sufficient condition for the asymptotic stability of the equilibrium point. The obtained condition is independent of time delays and relies on the network parameters of the neural system only. Therefore, the equilibrium and stability properties of the delayed neural network can be easily checked. We also make a detailed comparison between our result and the previous corresponding results derived in the previous literature. This comparison proves that our result is new and improves some of the previously reported robust stability results. Some illustrative numerical examples are given to show the applicability and advantages of our result.     

Boundary control for synchronization of fractional-order complex spatiotemporal networks based on PDEs with multiple delays and its application in image encryption

This paper addresses the synchronization problem of fractional-order complex spatiotemporal networks (CSNs) based on partial differential equations with delays via boundary control. First, fractional-order CSNs with time-invariant and time-varying delays are studied separately due to the widespread existence of time delays in complex networks. Moreover, two boundary controllers are proposed to solve the synchronization issue of fractional-order CSNs, in which nodes communicate with each other only on the spatial boundary. Furthermore, according to the fractional-order inequality, the synchronization criteria of fractional-order CNSs with multiple delays are obtained. Finally, the numerical simulations are given to verify the feasibility of the presented results. A case provides the application of CSNs in image encryption.     

Delay-dependent exponential stability for a class of neural networks with time delays and reaction-diffusion terms

In this paper, the exponential stability of a class of delayed neural networks described by nonlinear delay differential equations of the neutral type has been studied. By constructing appropriate Lyapunov functional and using the linear matrix inequality (LMI) optimization approach, a series of sufficient criteria is obtained ensuring the existence, uniqueness and global exponential stability of an equilibrium point of such a kind of delayed neural networks. These conditions are dependent on the size of the time delay and the measure of the space, which is usually less conservative than delay-independent and space-independent ones. And, these networks are generalized without assuming the boundedness and differentiability of the activate functions. The proposed LMI condition can be checked easily by recently developed algorithms. The results are new and improve the earlier work. Examples are provided to demonstrate the effectiveness and applicability of the proposed criteria.     

A modified Lyapunov functional with application to stability of neutral-type neural networks with time delays

This paper investigates the problem for stability of neutral-type dynamical neural networks involving delay parameters. Different form the previously reported results, the states of the neurons involve multiple delays and time derivative of states of neurons include discrete time delays. The stability of such neural systems has not been given much attention in the past literature due to the difficulty of finding Lyapunov functionals which are suitable for stability analysis of this type of neural networks. This paper constructs a generalized Lyapunov functional by introducing new terms into the well-known Lyapunov functional that enables us to conduct a theoretical investigation into stability analysis of delayed neutral-type neural systems. Based on this modified novel Lyapunov functional, sufficient criteria are derived, which guarantee the existence, uniqueness and global asymptotic stability of the equilibrium point of the neutral-type neural networks with multiple delays in the states and discrete delays in the time derivative of the states. The applicability of the proposed stability conditions rely on testing two basic matrix properties. The constraints impose on the system matrices are determined by using nonsingular M-matrix condition, and the constraints imposed on the coefficients of the time derivative of the delayed state variables are derived by exploiting the vector-matrix norms. We also note that the obtained stability conditions have no involvement with the delay parameters and expressed in terms of nonlinear Lipschitz activation functions. We present a constructive numerical example for this class of neural networks to give a systematic procedure for determining the imposed conditions on the whole system parameters of the delayed neutral-type neural systems.     

Identification of key genes and construction of CircRNA–miRNA–mRNA regulatory networks in osteoarthritis

BackgroundOsteoarthritis (OA) is one of the most frequent degenerative joint diseases with high rate of disability, but its mode of action remains largely unclear. The current study was aimed at identifying key genes and molecular mechanism in OA. Gene expression datasets (GSE1919, GSE55235, GSE55457) were downloaded from Gene Expression Omnibus for integrated bioinformatics analysis. Differentially expressed genes (DEGs) in OA synovial tissues were identified using GeoDiver and GEO2R. Gene ontology enrichment analyses were undertaken via FunRich and Metascape. Also, Gene Set Enrichment Analysis was performed using miRWalk3.0. Subsequently, pathways interrelation analysis of hub genes was carried out using plug-in ClueGO v2.3.3. Additionally, circRNA–miRNA–mRNA regulatory networks were visualized using Cytoscape.ResultsA total of 508 DEGs were obtained from three GSE datasets, of these five intersection DEGs (TNFAIP3, VEGFA, GADD45B, SIK1, KLF9) were shared by three GSE datasets. Intersection DEGs were significantly enriched in LKB1 signaling events, signaling events mediated by focal adhesion kinase, and PDGFR-beta signaling pathway. Enrichment analysis for all the DEGs showed that they mainly enriched in inflammatory response, cytokine production, blood vessel development, stress response, osteoclast differentiation, and MAPK signaling pathway. A total of 39 genes were regarded as hub genes and pathways interrelation analysis indicated that hub genes mainly enriched in TNF signaling pathway, IL-17 signaling pathway, and NF-kappa B signaling pathway.ConclusionsThe current study revealed the potential key genes, pathways, and circRNA–miRNA–mRNA regulatory networks in OA, which may contribute to a more comprehensive understanding of OA pathogenesis.How to cite: Li HZ, Xu XH, Lu HD. Identification of key genes and construction of CircRNA–miRNA–mRNA regulatory networks in osteoarthritis. Electron J Biotechnol 2019;37. https://doi.org/10.1016/j.ejbt.2018.11.004.     

Periodic solution to Cohen–Grossberg BAM neural networks with delays on time scales

In this paper, we investigate first the existence and uniqueness of periodic solution in a general Cohen–Grossberg BAM neural networks with delays on time scales by means of contraction mapping principle. Then by using the existence result of periodic solution and constructing a Lyapunov functional, we discuss the global exponential stability of periodic solution for above neural networks. In the last section, we also give examples to demonstrate the validity of our global exponential stability result of the periodic solution for above neural networks.