具常数存放的非线性功能反应捕食模型的定性分析

研究一类食饵具常数存放且功能反应函数为次线性函数的食饵-捕食者模型,获得了一些平衡点全局渐近稳定,极限环存在惟一的充分条件.利用数值仿真检验了我们的结论.     

一类具有脉冲效应的食饵依赖捕食系统分析

基于害虫防治,该文提出了一类具有脉冲效应的食饵依赖捕食系统并进行了分析,根据Floquet乘子理论,我们获得了害虫根除周期解全局渐近稳定与系统持续生存条件.     

食饵具有阶段结构的捕食-食饵系统的动力学行为

In this paper,we considered the predator-prey system with stage-structure for prey,where the predators predate immature preys only.The positivity and boundedness of the solutions and asymptotic stability of equilibrium were firstly discussed,and then uniformly persistent sufficient conditions of populations were found.     

一类变时滞离散Cohen-Grossberg神经网络模型周期解的存在性及其指数稳定性

利用重合度理论研究了一类变时滞的离散Cohen-Grossberg神经网络模型的周期解,并得到了模型周期解的全局指数稳定性的充分条件,推广了已有的结果,为神经网络的应用提供了重要的理论基础.最后给出一个例子进行数值模拟,数值模拟的结果更好地验证了结论.     

差分吸收光谱系统监测污染物浓度实时预测模型

差分吸收光谱(DOAS)法是一种有效的监测大气污染气体浓度的光学遥感方法,不仅有好的时间分辨率,而且测量灵敏度也很高。但是由于遥测系统处于复杂的大气环境中,各种干扰因素以及恶劣的气候条件,都会对系统产生影响。针对现有实时、在线监测差分吸收光谱系统中存在的不足,作者提出了一种基于改进Elman网络的实时预测模型。利用逐步回归筛选预测因子,不仅降低了预测网络的复杂程度,而且增强了系统的预测实时性。利用带自适应学习率的动态BP算法对改进的Elman网络进行训练,使预测系统能更好地辨识要预测的差分吸收光谱系统,该模型能较准确地对DOAS系统监测污染物数据进行实时跟踪监控,一定程度上弥补了遥测系统的不足。     

The Cauchy Problems and Global Solutions to the Deybe System and Burgers＇ Equations in Pseudomeasure Spaces

In this paper we consider the Cauchy problems of Burgers＇ equations and the Deybe system. Their existence and uniqueness of the time-global solutions for small initial data in some pseudomeasure spaces are obtained. The asymptotic stability of small solutions is proved. As an immediate result the existence and uniqueness of the self-similar solutions are also obtained provided the initial data satisfy the self-similar structures.     

The Global Convergence of Self-Scaling BFGS Algorithm with Nonmonotone Line Search for Unconstrained Nonconvex Optimization Problems

The self-scaling quasi-Newton method solves an unconstrained optimization problem by scaling the Hessian approximation matrix before it is updated at each iteration to avoid the possible large eigenvalues in the Hessian approximation matrices of the objective function. It has been proved in the literature that this method has the global and superlinear convergence when the objective function is convex （or even uniformly convex）. We propose to solve unconstrained nonconvex optimization problems by a self-scaling BFGS algorithm with nonmonotone linear search. Nonmonotone line search has been recognized in numerical practices as a competitive approach for solving large-scale nonlinear problems. We consider two different nonmonotone line search forms and study the global convergence of these nonmonotone self-scale BFGS algorithms. We prove that, under some weaker condition than that in the literature, both forms of the self-scaling BFGS algorithm are globally convergent for unconstrained nonconvex optimization problems.     

一类非线性两点边值问题的正解

该文研究了一类非线性两点边值问题正解的确切个数及全局结构, 证明了其解集本身构成一条连通分支.广泛地回答了文献[1]中提出的问题.     

基于指数分布的进化策略

典型的进化策略受自然进化过程的启发而成为求解全局优化问题的重要方法。传统的ES变异算子作为一个主要的进化技术是建立在正态分布的随机变量基础上的，本文提出了基于指数分布的进化策略由于采用了新的变异算子有效地减少了产生探试解的成本，从而优于传统的进化策略。