Zhigang Zeng

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In this paper, a general class of memristive neural networks with time delays is formulated and studied. Some sufficient conditions in terms of linear matrix inequalities are obtained, in order to achieve exponential stabilization. The result can be applied to the closed-loop control of memristive systems. In particular, several succinct criteria are given(More)
The paper introduces a general class of memristor-based recurrent neural networks with time-varying delays. Conditions on the nondivergence and global attractivity are established by using local inhibition, respectively. Moreover, exponential convergence of the networks is studied by using local invariant sets. The analysis in the paper employs results from(More)
This brief presents new theoretical results on the global exponential stability of neural networks with time-varying delays and Lipschitz continuous activation functions. These results include several sufficient conditions for the global exponential stability of general neural networks with time-varying delays and without monotone, bounded, or continuously(More)
We show that an n-neuron cellular neural network with time-varying delay can have 2(n) periodic orbits located in saturation regions and these periodic orbits are locally exponentially attractive. In addition, we give some conditions for ascertaining periodic orbits to be locally or globally exponentially attractive and allow them to locate in any(More)
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(More)
This paper presents new theoretical results on global exponential stability of recurrent neural networks with bounded activation functions and time-varying delays. The stability conditions depend on external inputs, connection weights, and time delays of recurrent neural networks. Using these results, the global exponential stability of recurrent neural(More)
In this paper, the multiperiodicity of a general class of discrete-time delayed neural networks (DTDNNs) is formulated and studied. Several sufficient conditions are obtained to ensure n-neuron DTDNNs can have 2<sup>n</sup> periodic orbits and these periodic orbits are locally attractive. In addition, we give the conditions for a periodic orbit to be(More)
The present paper introduces memristor-based fractional-order neural networks. The conditions on the global Mittag-Leffler stability and synchronization are established by using Lyapunov method for these networks. The analysis in the paper employs results from the theory of fractional-order differential equations with discontinuous right-hand sides. The(More)
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(More)