Tingwen Huang

Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects

This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.

Synchronization of delayed chaotic systems with parameter mismatches by using intermittent linear state feedback

This paper investigates the synchronization of coupled chaotic systems with time delay in the presence of parameter mismatches by using intermittent linear state feedback control. Quasi-synchronization criteria are obtained by means of a Lyapunov function and the differential inequality method. Numerical simulations on the chaotic systems are presented to demonstrate the effectiveness of the theoretical results.

Event-Triggering Sampling Based Leader-Following Consensus in Second-Order Multi-Agent Systems

In this note, the problem of second-order leader-following consensus by a novel distributed event-triggered sampling scheme in which agents exchange information via a limited communication medium is studied. Event-based distributed sampling rules are designed, where each agent decides when to measure its own state value and requests its neighbor agents broadcast their state values across the network when a locally-computed measurement error exceeds a state-dependent threshold. For the case of fixed topology, a necessary and sufficient condition is established. For the case of switching topology, a sufficient condition is obtained under the assumption that the time-varying directed graph is uniformly jointly connected. It is shown that the inter-event intervals are lower bounded by a strictly positive constant, which excludes the Zeno-behavior before the consensus is achieved. Numerical simulation examples are provided to demonstrate the correctness of theoretical results.

Exponential stability analysis of memristor-based recurrent neural networks with time-varying delays

This paper investigates the exponential stability problem about the memristor-based recurrent neural networks. Having more rich dynamic behaviors, neural networks based on the memristor will play a key role in the optimistic computation and associative memory, therefore, stability analysis of memristor-based neural networks are quite important. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent neural networks is established; and the stability of memristor-based neural networks with time-varying delays is studied. Several sufficient conditions for the global exponential stability of these neural networks are presented. These results ensure global exponential stability of memristor-based neural networks in the sense of Filippov solutions. In addition to providing criteria for memristor-based neural networks with time-varying delays, these stability conditions can also be used for memristor-based neural networks with constant time delays or without time delays. Furthermore, it is convenient to estimate the exponential convergence rates of this neural network by using the results. An illustrative example is given to show the effectiveness of the obtained results.

Multistability of Recurrent Neural Networks With Time-varying Delays and the Piecewise Linear Activation Function

In this brief, stability of multiple equilibria of recurrent neural networks with time-varying delays and the piecewise linear activation function is studied. A sufficient condition is obtained to ensure that n-neuron recurrent neural networks can have (4k-1)n equilibrium points and (2k)n of them are locally exponentially stable. This condition improves and extends the existing stability results in the literature. Simulation results are also discussed in one illustrative example.

Exponential Adaptive Lag Synchronization of Memristive Neural Networks via Fuzzy Method and Applications in Pseudorandom Number Generators

This paper investigates the problem of exponential lag synchronization control of memristive neural networks (MNNs) via the fuzzy method and applications in pseudorandom number generators. Based on the knowledge of memristor and recurrent neural networks, the model of MNNs is established. Then, considering the state-dependent properties of memristor, a fuzzy model of MNNs is employed to provide a new way of analyzing the complicated MNNs with only two subsystems, and update laws for the connection weights of slave systems and controller gain are designed to make the slave systems exponentially lag synchronized with the master systems. Two examples about synchronization problems are presented to show the effectiveness of the obtained results, and an application of the obtained theory is also given in the pseudorandom number generator.

Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays

This paper deals with the problem of global exponential synchronization of a class of memristor-based recurrent neural networks with time-varying delays based on the fuzzy theory and Lyapunov method. First, a memristor-based recurrent neural network is designed. Then, considering the state-dependent properties of the memristor, a new fuzzy model employing parallel distributed compensation (PDC) gives a new way to analyze the complicated memristor-based neural networks with only two subsystems. Comparisons between results in this paper and in the previous ones have been made. They show that the results in this paper improve and generalized the results derived in the previous literature. An example is also given to illustrate the effectiveness of the results.

Differential evolution based on covariance matrix learning and bimodal distribution parameter setting

Point out the drawbacks of the crossover operator and the parameter settings of differential evolution (DE).Propose a novel DE variant based on covariance matrix learning and bimodal distribution parameter setting, named CoBiDE.Verify the effectiveness of CoBiDE by many experiments. Differential evolution (DE) is an efficient and robust evolutionary algorithm, which has been widely applied to solve global optimization problems. As we know, crossover operator plays a very important role on the performance of DE. However, the commonly used crossover operators of DE are dependent mainly on the coordinate system and are not rotation-invariant processes. In this paper, covariance matrix learning is presented to establish an appropriate coordinate system for the crossover operator. By doing this, the dependence of DE on the coordinate system has been relieved to a certain extent, and the capability of DE to solve problems with high variable correlation has been enhanced. Moreover, bimodal distribution parameter setting is proposed for the control parameters of the mutation and crossover operators in this paper, with the aim of balancing the exploration and exploitation abilities of DE. By incorporating the covariance matrix learning and the bimodal distribution parameter setting into DE, this paper presents a novel DE variant, called CoBiDE. CoBiDE has been tested on 25 benchmark test functions, as well as a variety of real-world optimization problems taken from diverse fields including radar system, power systems, hydrothermal scheduling, spacecraft trajectory optimization, etc. The experimental results demonstrate the effectiveness of CoBiDE for global numerical and engineering optimization. Compared with other DE variants and other state-of-the-art evolutionary algorithms, CoBiDE shows overall better performance.

Circuit design and exponential stabilization of memristive neural networks

This paper addresses the problem of circuit design and global exponential stabilization of memristive neural networks with time-varying delays and general activation functions. Based on the Lyapunov-Krasovskii functional method and free weighting matrix technique, a delay-dependent criteria for the global exponential stability and stabilization of memristive neural networks are derived in form of linear matrix inequalities (LMIs). Two numerical examples are elaborated to illustrate the characteristics of the results. It is noteworthy that the traditional assumptions on the boundness of the derivative of the time-varying delays are removed.

Passivity-based synchronization of a class of complex dynamical networks with time-varying delay

This paper proposes a complex delayed dynamical network consisting of N linearly and diffusively coupled identical reaction-diffusion neural networks. By utilizing some inequality techniques, a sufficient condition ensuring the output strict passivity is derived for the proposed network model. Then, we reveal the relationship between output strict passivity and synchronization of the proposed network model. Moreover, based on the obtained passivity result and the relationship between output strict passivity and synchronization, a criterion for synchronization is established. Finally, a numerical example is provided to illustrate the correctness and effectiveness of the proposed results.