Shiping Wen

Synchronization control of a class of memristor-based recurrent neural networks

In this paper, we formulate and investigate a class of memristor-based recurrent neural networks. Some sufficient conditions are obtained to guarantee the exponential synchronization of the coupled networks based on drive-response concept, differential inclusions theory and Lyapunov functional method. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. Finally, the validity of the obtained result is illustrated by a numerical example.

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.

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.

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.

Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in Image Encryption

This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption.

Dynamics Analysis of a Class of Memristor-Based Recurrent Networks with Time-Varying Delays in the Presence of Strong External Stimuli

In this paper, we investigate the dynamics problem about the memristor-based recurrent network with bounded activation functions and bounded time-varying delays in the presence of strong external stimuli. It is shown that global exponential stability of such networks can be achieved when the external stimuli are sufficiently strong, without the need for other conditions. A sufficient condition on the bounds of stimuli is derived for global exponential stability of memristor-based recurrent networks. And all the results are in the sense of Filippov solutions. Simulation results illustrate the uses of the criteria to ascertain the global exponential stability of specific networks.

Passivity analysis of memristor-based recurrent neural networks with time-varying delays ☆

Abstract This paper investigates the delay-dependent exponential passivity problem of the memristor-based recurrent neural networks (RNNs). Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based RNNs is established. Taking into account of the information of the neuron activation functions and the involved time-varying delays, several improved results with less computational burden and conservatism have been obtained in the sense of Filippov solutions. A numerical example is presented to show the effectiveness of the obtained results.

Event-Triggering Load Frequency Control for Multiarea Power Systems With Communication Delays

This paper studies the load frequency control (LFC) for power systems with communication delays via an event-triggered control method to reduce the amount of communications required. The effect of the load disturbances on the augmented output is defined as a robust performance index of the augmented LFC scheme. By utilizing a time-delayed system design approach, a new model of the LFC scheme with delays is formulated, where the communication delays and event-triggered control are integrated for the LFC scheme. Based on the Lyapunov-Krasovskii functional method, the criteria for the event-triggered stability analysis and control synthesis of the LFC scheme are derived. Finally, the effectiveness of the proposed method is verified by simulation studies.

Dynamic behaviors of memristor-based delayed recurrent networks

This paper investigates the problem of the existence and global exponential stability of the periodic solution of memristor-based delayed network. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent networks is established. Several sufficient conditions are obtained, which ensure the existence of periodic solutions and global exponential stability of the memristor-based delayed recurrent networks. These results ensure global exponential stability of memristor-based network in the sense of Filippov solutions. And, it is convenient to estimate the exponential convergence rates of this network by the results. An illustrative example is given to show the effectiveness of the theoretical results.