In this paper, we study the existence and global exponential stability of almost periodic solution for memristor-based neural networks with leakage, time-varying and distributed delays. Using a new Lyapunov function m...
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In this paper, we study the existence and global exponential stability of almost periodic solution for memristor-based neural networks with leakage, time-varying and distributed delays. Using a new Lyapunov function method, we prove that this delayed neural network has a unique almost periodic solution, which is globally exponentially stable. Moreover, the obtained conclusion on the almost periodic solution is applied to prove the existence and stability of periodic solution (or equilibrium point) for this delayed neural network with periodic coefficients (or constant coefficients).
A nuclear export signal (NES) is a sequence of amino acids, which is a protein localization signal, and contributes to regulate localization of cellular proteins. In recent peering works, activity of NESs were introdu...
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This paper addresses the multistability for a general class of recurrent neural networks with time-varying delays. Without assuming the linearity or monotonicity of the activation functions, several new sufficient con...
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This paper addresses the multistability for a general class of recurrent neural networks with time-varying delays. Without assuming the linearity or monotonicity of the activation functions, several new sufficient conditions are obtained to ensure the existence of (2K+1)(n) equilibrium points and the exponential stability of (K+1)(n) equilibrium points among them for n-neuron neural networks, where K is a positive integer and determined by the type of activation functions and the parameters of neural network jointly. The obtained results generalize and improve the earlier publications. Furthermore, the attraction basins of these exponentially stable equilibrium points are estimated. It is revealed that the attraction basins of these exponentially stable equilibrium points can be larger than their originally partitioned subsets. Finally, three illustrative numerical examples show the effectiveness of theoretical results.
Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system wit...
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Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a spacetime tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.
Spiking neural P systems with weights(WSN P systems,for short)are a new variant of spiking neural P systems,where the rules of a neuron are enabled when the potential of that neuron equals a given *** is known that WS...
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Spiking neural P systems with weights(WSN P systems,for short)are a new variant of spiking neural P systems,where the rules of a neuron are enabled when the potential of that neuron equals a given *** is known that WSN P systems are universal by simulating register ***,in these universal systems,no bound is considered on the number of neurons and *** this work,a restricted variant of WSN P systems is considered,called simple WSN P systems,where each neuron has only one *** complexity parameter,the number of neurons,to construct a universal simple WSN P system is *** is proved that there is a universal simple WSN P system with 48 neurons for computing functions;as generator of sets of numbers,there is an almost simple(that is,each neuron has only one rule except that one neuron has two rules)and universal WSN P system with 45 neurons.
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 co...
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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.
作者：
Sun, YangguangCai, ZhihuaCollege of Computer Science
South-Central University for Nationalities Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Software Engineering
Wuhan University College of Computer Science South-Central University for Nationalities Wuhan 430072 China
For the structural characteristics of Chinese NvShu character, by combining the basic idea in LLT local threshold algorithm and introducing the maximal betweenclass variance algorithm into local windows, an improved c...
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A novel statistical method using path integral Monte Carlo simulation based on quantum mechanics to detect edges of interested objects was proposed in this paper. Our method was inspired by essential characteristics o...
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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 functio...
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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.
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