Slave Neural Networks Research Articles

Finite-time synchronisation for Markovian master-slave neural networks with weighted try-once-discard protocol and static intermittent control

ABSTRACT This paper studies the finite-time synchronisation (FTS) issue for Markovian master-slave neural networks (NNs). A weighted try-once-discard (WTOD) protocol is introduced to overcome communication constraints, and an intermittent control strategy is employed to surmount energy constraints. A WTOD and parameter dependent static feedback controller is designed for the slave neural networks. Then, sufficient conditions are developed to achieve FTS for the considered master-slave NNs, and the controller design method is obtained. Finally, a numerical example is provided to demonstrate and verify the derived results.

Synchronization for neural networks over event-triggered multi-channel: Relay channels under cyber-attacks

This work addresses synchronization for master–slave neural networks (MSNNs) with remote transmission. To reduce the communication frequency, an event-triggered strategy combined with a dynamically adjusted threshold is employed. For the sake of enhancing the information transmission quality, a relay channels protocol that considers cyber-attacks is established, acknowledging that occurrence rates of cyber-attacks may vary across different channels. Furthermore, regarding the limited capacity of channels, the current research considers a multi-channel transmission mechanism, where the transmission arrangement for each channel relies on the occurrence rates of denial-of-service (DoS) attacks. A sufficient condition is then proposed to guarantee the synchronization of MSNNs. Finally, an example is adopted to substantiate the efficiency of the obtained results.

Asynchronous finite-time extended dissipative sliding mode control for semi-Markovian jump master–slave neural networks

This paper aims to investigate the finite-time extended dissipative asynchronous sliding mode control (SMC) on semi-Markovian jump master–slave neural networks (MSNNs) subject to event-triggering scheme (ETS) and packet dropout. Considering ETS, packet dropout and network-induced delays, the asynchronous finite-time SMC which is more realistic is designed. Sufficient conditions are obtained that closed-loop system is extended dissipative stochastic finite-time bound. Meantime, we present novel results for closed-loop system on extended dissipative stochastic finite-time contractive bound. Finally, the biological neural network is presented to illustrate the validness of methods.

Novel criteria of sampled-data synchronization controller design for gated recurrent unit neural networks under mismatched parameters

Synchronization between neural networks (NNs) has been intensively investigated to analyze stability, convergence properties, neuronal behaviors and response to various inputs. However, synchronization techniques of NNs with gated recurrent units (GRUs) have not been provided until now due to their complicated nonlinearity. In this paper, we address the sampled-data synchronization problems of GRUs for the first time, and propose controller design methods using discretely sampled control inputs to synchronize master and slave GRUs. The master and slave GRUs are mathematically modeled as a linear parameter varying (LPV) system in which the parameter of the slave GRUs is constructed independently of the master GRUs. This distinctive modeling feature provides flexibility to extend the existing master and slave NNs into a more general structure. Indeed, the sampled-data synchronization can be achieved by formulating the design condition in terms of linear matrix inequalities (LMIs). The novel sampled-data synchronization criteria are devised by combining the H∞ controller design with the looped-functional approach. The synthesized synchronization controllers guarantee not only asymptotic stability of the synchronization error system with aperiodic sampling, but also provides a satisfactory H∞ control performance. Moreover, the communication efficiency is improved by using the proposed method in which the sampled-data synchronization controller is combined with the event-triggered mechanism. Finally, the numerical example validates the proposed theoretical contributions via simulation results.

Event-based fixed-time synchronization of neural networks under DoS attack and its applications

In this paper, the fixed-time synchronization control for neural networks with discontinuous data communication is investigated. Due to the transmission blocking caused by DoS attack, it is intractable to establish a monotonically decreasing Lyapunov function like the conventional analysis of fixed-time stability. Therefore, by virtue of recursive and reduction to absurdity approaches, novel fixed-time stability criteria where the estimated upper bound of settling-time is inherently different from existing results are presented. Then, based on the developed conditions, an event-triggered control scheme that can avoid Zeno behavior is designed to achieve synchronization of master–slave neural networks under DoS attack within a prescribed time. For comparison, the established control scheme is further discussed under the case without DoS attack, and the circumstance that there is no attack or event-triggered mechanism, respectively. Simulation results are finally provided to illustrate the significant and validity of our theoretical research. Especially, in terms of encryption and decryption keys generated from the synchronization behavior of chaotic networks, we specifically discuss the application of the proposed fixed-time synchronization scheme to image and audio encryption.

Bounded synchronization for uncertain master–slave neural networks: An adaptive impulsive control approach

This paper investigates the bounded synchronization of the discrete-time master–slave neural networks (MSNNs) with uncertainty. To deal with the unknown parameter in the MSNNs, a parameter adaptive law combined with the impulsive mechanism is proposed to improve the estimation efficiency. Meanwhile, the impulsive method also is applied to the controller design for saving the energy. In addition, a novel time-varying Lyapunov functional candidate is employed to depict the impulsive dynamical characteristic of the MSNNs, wherein a convex function related to the impulsive interval is used to obtain a sufficient condition for bounded synchronization of the MSNNs. Based on the above condition, the controller gain is calculated utilizing an unitary matrix. An algorithm is proposed to reduce the boundary of the synchronization error by optimizing its parameters. Finally, a numerical example is provided to illustrate the correctness and the superiority of the developed results.

New Results on Robust Synchronization for Memristive Neural Networks with Fractional Derivatives via Linear Matrix Inequality

This article mainly concentrates on the synchronization problem for a more general kind of the master–slave memristor-based neural networks with fractional derivative. By applying a continuous-frequency-distributed equivalent model tool, some new outcomes and sufficient conditions on the robust synchronization of the master–slave neural networks with uncertainty are proposed via linear matrix inequality (LMI). Finally, two memristive neural networks model with fractional derivatives are presented to validate the efficiency of the theoretical results.

Resilient fixed-time synchronization of neural networks under DoS attacks

In this paper, the fixed-time synchronization of neural networks (NNs) with denial-of-service (DoS) attacks is investigated. A switching system with a stable subsystem and an unstable subsystem is utilized to describe the error dynamic behaviours between the master and slave NNs with DoS attacks. By virtue of the comparison principle, a novel fixed-time stability lemma for the switching system is established. The estimation of upper bound of setting time is provided. We further apply this lemma to investigate the resilient fixed-time synchronization of NNs subject to DoS attacks. By designing an appropriate controller, some sufficient criteria are proposed to guarantee the synchronization within a prearranged fixed time. The effects of DoS attacks on the settling time are discussed as well. Two numerical examples are given to show the effectiveness of main results and a potential application in the secure communication is provided.

Modified function projective synchronization of master-slave neural networks with mixed interval time-varying delays via intermittent feedback control

<abstract><p>The modified function projective synchronization problem for master-slave neural networks with mixed interval time-varying delays is presented using periodically intermittent feedback control. The interval distributed time-varying delay including the lower and upper bound is comprehensively established, which developed from the previous work. The following techniques are utilize to analyze the appropriate criteria for the modified function projective synchronization problem for master-slave neural networks with mixed interval time-varying delays such as the construction of appropriate Lyapunov-Krasovskii functionals merged with Newton-Leibniz formulation method, the intermittent feedback control technique, the reciprocally convex technique's lower bound lemma, Jensen's inequality, and the piecewise analytic method. The sufficient criteria for the modified function projective synchronization of the error system between the master and slave neural networks with intermittent feedback control are first established in terms of linear matrix inequalities (LMIs). The designed controller ensures that the synchronization of the error systems are proposed via intermittent feedback control. Finally, numerical examples are given to demonstrate the effectiveness of the proposed method.</p></abstract>

Equivalent-input-disturbance estimator-based event-triggered control design for master–slave neural networks

This paper investigates the robust synchronization problem for a class of master–slave neural networks (MSNNs) subject to network-induced delays, unknown time-varying uncertainty, and exogenous disturbances. An equivalent-input-disturbance (EID) estimation technique is applied to compensate for the effects of unknown uncertainty and disturbances in the system output. In addition, to reduce the burden of the communication channel in the addressed MSNNs and improve the utilization of bandwidth an event-triggered control protocol is developed to obtain the synchronization of MSNNs. In particular, event-triggering conditions are verified periodically at every sampling instant in both sensors and actuators to avoid the Zeno behavior in the networks. By designing an appropriate low-pass filter in the EID estimator block, the accuracy of disturbance estimation performance is improved. Moreover, by concatenating the synchronization error, observer, and filter states as a single state vector, an augmented system is formulated. Then the tangible delay-dependent stability condition for that augmented system is established by employing the Lyapunov stability theory and reciprocally convex approach. Based on the feasible solutions of the derived stability conditions, the event-triggering parameters, controller, and observer gains are co-designed. Finally, two toy examples are given to illustrate the established theoretical findings.

Quasisynchronization for Neural Networks With Partial Constrained State Information via Intermittent Control Approach.

This work addresses quasisynchronization (QS) of the master-slave (MS) neural networks (NNs) with mismatched parameters. The logarithmic quantizer and the round-robin protocol (RRP) are used to deal with the limited communication channel (CC) capacity, then the intermittent control strategy is employed to improve the efficiency of CC and the controller. A transmission-dependent controller is designed, and the synchronization error system (SES) is established. The QS with a boundary is ensured for the MS NNs by a developed sufficient condition, and the controller design method is given. A numerical simulation is given to show the effectiveness of the obtained method.

Quantization synchronization of chaotic neural networks with time delay under event-triggered strategy.

This paper shows solicitude for the quantization synchronization of delayed chaotic master and slave neural networks under an dynamic event-triggered strategy. In virtue of a generalized Halanay-type inequality, a theoretical criterion for quasi-synchronization of master and slave neural networks is derived. Meanwhile, we can obtain an exact upper bound of synchronization error by using this criterion. Compared with output feedback controller with event triggering and quantization, the case where the controller only affected by quantization is also considered. Then, we exclude the Zeno behavior of the event-triggered controller. A sufficient criterion for the existence of the quantized output feedback controllers is also provided. A numerical example is cited to illustrate the efficiency of our theoretical criteria. In addition, some experiments of secure image communication are conducted under quasi-synchronization of master and slave neural networks.

Extended dissipativity of semi-Markov jump neural networks with partly unknown transition rates

In this paper, the synchronization problem is addressed for semi-Markov jump master–slave neural networks (SMJNN) with extended (X,Y,Z)-dissipativity performance and partly unknown transition rates (TR). Comparing with constant TR in Markovian jump neural networks, the TR of SMJNN depend on the sojourn time (ST) of semi-Markov process. First, some sufficient stability and extended (X,Y,Z)-dissipativity criteria are established for the error system with partly unknown TR. Then, ST-dependent dissipativity criterion is transformed to feasible condition by the upper and lower bounds of TR. Furthermore, the state feedback controller is also designed to ensure the synchronization of SMJNN. Finally, a numerical example is provided to verify the admissibility and effectiveness of the acquired results.

Event-Triggered Output Feedback Synchronization of Master-Slave Neural Networks Under Deception Attacks.

The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels from the sensor to controller and from controller to actuator are subject to stochastic deception attacks modeled by two independent Markov processes. Two discrete event-triggered mechanisms are introduced for both channels to reduce the number of data transmission through the communication channels. To comply with practical point of view, static output feedback is utilized. By employing the Lyapunov-Krasovskii functional method, some sufficient conditions on the synchronization of master-slave neural networks are derived in terms of linear matrix inequalities, which make it easy to design suitable output feedback controllers. Finally, a numerical example is presented to show the effectiveness of the proposed method.

Spatio-temporal synchronization of reaction–diffusion BAM neural networks via impulsive pinning control

This paper is concerned with the synchronization of bi-directional associative memory (BAM) neural networks with diffusion and time-varying delays, in which both synchronizing and desynchronizing impulses are considered. Due to the existence of the diffusion effect in artificial neural networks, synchronization control involves the dynamical behaviors of master–slave systems both in space and time. An impulsive pinning controller is designed for realizing the spatio-temporal synchronization of neural networks. By the Lyapunov functional theory and the comparison principle, exponential synchronization criteria are established, which consist of algebraic inequalities and are easy to implement. Two numerical examples show that controlling only a portion of neurons in the slave neural network can achieve the synchronization of master–slave systems.

Robust synchronisation control of discontinuous CGNNs with time-varying delays

Fixed-time synchronisation requires that the settling time is independent of the initial synchronisation errors and the upper bound of the settling time can be given in advance. From this point of view, the fixed time synchronisation is more favourable and applicable. This paper aims to investigate the fixed-time synchronisation and fixed-time anti-synchronisation of Cohen-Grossberg master-slave neural networks with discontinuous neuron activations and time delays. For the purpose of fixed-time synchronisation, under the framework of Filippov solution, by using the Lyapunov-Krasovskii functional approach and designing a novel discontinuous feedback control procedure for the slave neural networks, some sufficient criteria are firstly derived to design the control parameters for achieving synchronisation within a fixed time for the proposed discontinuous Cohen-Grossberg neural networks. Then, based on the fixed-time synchronisation, another new discontinuous feedback controller is also designed to accomplish the fixed-time anti-synchronisation for the considered neural system. Finally, two numerical examples associated with main theorems and corollaries are presented to illustrate the proposed methodologies.

Quasi-Synchronization of Time Delay Markovian Jump Neural Networks With Impulsive-Driven Transmission and Fading Channels.

The problem of quasi-synchronization (QS) for the Markovian jump master-slave neural networks with time-varying delay is studied in this article, where the mismatch parameters and unreliable communication channels are considered as well. A set of stochastic variables with different expectations are used to describe the fading phenomena of parallel communication channels. An impulsive-driven transmission strategy is designed to reduce the communication load, and a corresponding impulsive controller is then designed. A synchronization error system (SES) is obtained, and a convex QS condition is established for the SES. A linear matrix inequality-based iterative algorithm is proposed to reduce the bound of the SES, and the corresponding controller gains are calculated. A numerical example is provided to illustrate the effectiveness of the developed result.

Secure Communication Based on Quantized Synchronization of Chaotic Neural Networks Under an Event-Triggered Strategy.

This article presents a secure communication scheme based on the quantized synchronization of master-slave neural networks under an event-triggered strategy. First, a dynamic event-triggered strategy is proposed based on a quantized output feedback, for which a quantized output feedback controller is formed. Second, theoretical criteria are derived to ensure the bounded synchronization of master-slave neural networks. With these criteria, an explicit upper bound is given for the synchronization error. Sufficient conditions are also provided on the existence of quantized output feedback controllers. A Chua's circuit is chosen to illustrate the effectiveness of our theoretical results. Third, a secure communication scheme is presented based on the synchronization of master-slave neural networks by combining the basic principle of cryptology. Then, a secure image communication is studied to verify the feasibility and security performance of the proposed secure communication scheme. The impact of the quantization level and the event-triggered control (ETC) on image decryption is investigated through experiments.

Finite-time anti-synchronization of neural networks with time-varying delays via inequality skills

In this paper, we consider the finite-time anti-synchronization for the master–slave neural networks with time delays. By means of combining integral inequality skills with other inequality skills, two novel criteria to assure the finite-time anti-synchronization for the discussed master–slave neural networks are presented under two classes of different controllers. Our results and method are different from those in existing papers.

Finite-Horizon l2-l∞ Synchronization for Time-Varying Markovian Jump Neural Networks Under Mixed-Type Attacks: Observer-Based Case.

This paper studies the synchronization issue of time-varying Markovian jump neural networks (NNs). The denial-of-service (DoS) attack is considered in the communication channel connecting master NNs and slave NNs. An observer is designed based on the measurements of master NNs transmitted over this unreliable channel to estimate their states. The deception attack is used to destroy the controller by changing the sign of the control signal. Then, the mixed-type attacks are expressed uniformly, and a synchronization error system is established using this function. A finite-horizon l2-l∞ performance is proposed, and sufficient conditions are derived to ensure that the synchronization error system satisfies this performance. The controllers are then obtained by a recursive linear matrix inequality algorithm. At last, a simulation result to show the feasibility of the developed results is given.