考虑疾病传播过程中的随机干扰,运用随机人口建模中参数扰动的标准化技术,建立了一类具有随机扰动的传染病SEIR(susceptible-exposed but not infectious-infectious-removed)模型,证明了模型解的存在唯一性及非负性,并研究了无病平衡...考虑疾病传播过程中的随机干扰,运用随机人口建模中参数扰动的标准化技术,建立了一类具有随机扰动的传染病SEIR(susceptible-exposed but not infectious-infectious-removed)模型,证明了模型解的存在唯一性及非负性,并研究了无病平衡点满足p阶矩指数稳定的条件.研究结果为传染病预防与控制提供一定的理论依据与决策支持.展开更多
The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). Th...The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). The results obtained provide an easily verified guideline for determining the exponential robust stability of delayed neural networks. The theoretical analysis and numerical simulations show that the results are less conservative and less restrictive than those reported recently in the literature.展开更多
基金Supported by the Natural Science Foundation of China (10671047)the Science Foundation of the Education Department of Hei-longjiang Province(11541269)
文摘考虑疾病传播过程中的随机干扰,运用随机人口建模中参数扰动的标准化技术,建立了一类具有随机扰动的传染病SEIR(susceptible-exposed but not infectious-infectious-removed)模型,证明了模型解的存在唯一性及非负性,并研究了无病平衡点满足p阶矩指数稳定的条件.研究结果为传染病预防与控制提供一定的理论依据与决策支持.
文摘The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). The results obtained provide an easily verified guideline for determining the exponential robust stability of delayed neural networks. The theoretical analysis and numerical simulations show that the results are less conservative and less restrictive than those reported recently in the literature.
