Fengjian Yang

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In this paper we study the globally exponential stability of a class of neural networks with time-varying delays. Applying idea of vector Lyapunov function, Young inequality and Halanay differential inequality with delay, the sufficient conditions for the existence and globally exponential stability of the equilibrium point are obtained
Naringin exhibits antiinflammatory activity and is shown to induce bone formation. Yet the impact of naringin on inflammation-affected bone marrow-derived mesenchymal stem cell (BM-MSC), a promising tool for the regenerative treatment of bone injury, remained to be investigated. We first cultured and characterized the BM-MSCs in vitro and observe the(More)
Existing topic tracking methods are mostly for news and forum data, which lack of statistical methods for microblogging on relevant topics. Combined with characteristics of micro-blog information, the paper proposes a microblogging statistical methods based on semantic similarity. Firstly by building topic semantic model and then use the HowNet semantic(More)
Some sufficient conditions are obtained for the global exponential stability of a solution and existence of the periodic solution to the general bidirectional associative memory (BAM) neural networks with distributed delay and impulsive by using the Lyapunov functional and some inequality technique. These results are helpful for designing a globally(More)
This paper is concerned with the stability of the impulsive bidirectional associative memory (BAM) neural networks with time delays. By means of Lyapunov function and analysis technique, sufficient conditions are obtained for the existence and uniformly stability of a unique equilibrium solution without assuming the activation function to be bounded,(More)
In this paper, a model of impulsive BAMs neural networks is first formulated. We investigate impulsive effects on the stability of BAMs neural networks with delays and obtain some sufficient conditions ensuring exponential stability of the impulsive delay system.The results extend and improve some recent works for impulsive neural networks as well as(More)