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In Chapter 6, based on discretization technique an implementable algorithm for nonconvex generalized semi-infinite minimax problems is presented and, utilizing properties of generalized quasi-directional derivative, its global convergence is proven under weak conditions.

对于广义极大极小问题,本文第六章在较弱的条件下,利用广义伪方向导数的性质,用离散化的技巧给出了非凸广义半无限极大极小问题的一种可实现的全局收敛算法。

Chapter 2 deals with some refinements of the central limit theorem for a class of non-uniformly hyperbolic dynamical systems called Youngs system, such as local central limit theorem and so-called Berry-Esseen theorem giving the rate of convergence in the central limit theorem.

在第二、三章中,我们考虑一类重要的非一致双曲动力系统的统计性质-中心极限定理,及其进一步的精细结果如局部中心极限定理,带有收敛速度的中心极限定理。

Chapter 2 deals with some refinements of the central limit theorem for a class of non-uniformly hyperbolic dynamical systems called Young\'s system, such as local central limit theorem and so-called Berry-Esseen theorem giving the rate of convergence in the central limit theorem.

在第二、三章中,我们考虑一类重要的非一致双曲动力系统的统计性质-中心极限定理,及其进一步的精细结果如局部中心极限定理,带有收敛速度的中心极限定理。

Based on ear recognition, an improved NMFSC(Nonnegative Matrix Factorization with Sparseness Constraints) method was proposed by imposing an additional constraint on the objective function of NMFSC, which could capture the semantic relations of coefficient matrix as orthogonal as possible. The interated rules to solve the objective function with the constraint were presented, and its convergence was proved.

针对人耳识别问题,提出了一种改进的稀疏性受限的非负矩阵因子方法,通过增加一个使系数矩阵尽可能正交的约束条件来定义原目标函数,给出求解该目标函数的迭代规则,并证明迭代规则的收敛性。

Based on ear recognition, an improved NMFSC (Non-negative Matrix Factorization with Sparseness Constraints) method was proposed by imposing an additional constraint on the objective function of NMFSC, which could capture the semantic relations of coefficient matrix as orthogonal as possible. The interated rules to solve the objective function with the constraint were presented, and its convergence was proved.

针对人耳识别问题,提出了一种改进的稀疏性受限的非负矩阵因子方法,通过增加一个使系数矩阵尽可能正交的约束条件来定义原目标函数,给出求解该目标函数的迭代规则,并证明迭代规则的收敛性。

Are also given by constructing Lyapunov function. Finally, by applying the energy methods, Sobolev embedding theorems and bootstrap arguments, the global existence of nonnegative classical solutions to equations with homogeneous Neumann boundary value condition is proved when the space dimension is at most 5. Under certain conditions for the coefficients of the reaction functions, the convergence of the solutions is established for the system with large diffusion coefficients by constructing Lyapunov function.

的正平衡点全局渐近稳定的充分条件;最后,当空间维数不超过5时,应用能量估计、Sobolev嵌入定理和bootstrap技巧证明在齐次Neumann边值条件下非负古典解的整体存在性,并通过构造Lyapunov函数给出当反应函数的系数满足一定条件、扩散系数较大时该模型解的收敛性。

The step size of the new algorithm is inversely with the summation of squared norm of the error vector and β/λ.

小的步长μ可以确保稳态时具有小的失调,但是算法的收敛速度慢,并且对非稳态系统的跟踪能力差。

Motivated by the newψfunctional stability,we can establish some new kinds of stabilities which are different from the exponential stability.With the help of semimartingale convergence theorem,stochastic calculous,moment inequality,Burkholder-Davis-Gundy inequality,Gronwall inequality etc.,we find that the product ofψfunction and the solutions of neutral equations will approach a nonempty set if some specific conditions are satisfied.

我们应用半鞅收敛定理、随机微积分的相关知识以及矩不等式、Burkholder-Davis-Gundy不等式和Gronwall不等式等技巧,得到中立型随机泛函微分方程的解与特定的"ψ函数"的乘积在一定的条件下将趋向某非空集合这一结论,并建立了该中立型方程的随机LaSalle型定理。

And then obtained under some conditions of probability are some sufficient conditions for moment complete convergence for independent non-identically distributed random elements in a separable Banach space which are stochastically bounded by a positive random variable.

文章在随机元序列随机有界于某非负随机变量的条件下,引入慢变化函数l和选定的函数类S,在一定的概率性条件下,得到B值独立不同分布随机元序列矩完全收敛性的充分条件,推广了有关文献的结果。

For the coordination of multiple agents in noncooperation environment, we considered the learning aim as finding a Nash equilibrium strategy through in learning function taking other agent's actions into account. Therefore, we expended the single agent Q-learning algorithm into multiagent Q-learning algorithm. In addition, we proved the convergence of multi-agent Q-learning algorithm under certain general sum stochastic game architecture for only one Nash equilibrium or multiple same Nash equilibrium.

对于非合作环境下的多智能体协调我们通过在学习函数中考虑了智能体的联合行动,提出把学习目标作为求取一个Nash平衡点策略,这样将单智能体Q-learning算法扩展到多智能体的Q-learning算法;证明了在一定的一般和随机对策结构下具有一个或多个相同Nash平衡点条件下多智能体Q-learning算法的收敛性。

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推荐网络例句

Plunder melds and run with this jewel!

掠夺melds和运行与此宝石!

My dream is to be a crazy growing tree and extend at the edge between the city and the forest.

此刻,也许正是在通往天国的路上,我体验着这白色的晕旋。

When you click Save, you save the file to the host′s hard disk or server, not to your own machine.

单击"保存"会将文件保存到主持人的硬盘或服务器上,而不是您自己的计算机上。