先学规则，即定理，运行的次序，牛顿定律-然后在章节的最后列一张问题列表。
Learn the rules first - the theorems, the order of operations, Newton's laws - then make a run at the problem list at the end of the chapter.
最后，研究了直觉模糊相似关系，证明了直觉模糊相似矩阵的一个定理。
Finally, intuitionistic fuzzy relations are investigated with the proof of a theorem on intuitionistic fuzzy similar matrix.
最后，基于此定理，给出了选址问题的一个多项式2近似算法。
Finally, based on the theorem, a polynomial 2 approximation algorithm for the location problem is presented.
最后，给出了广义模糊粗糙近似算子的分解与合成定理。
At last, we we prove the decomposition and synthetize theorems of general fuzzy rough approximation operator.
最后，这个定理被证明是错误的。
最后，在较弱的假设条件下，讨论了G -凸空间中的重合点组定理与极大极小组定理，从而推广了近期文献的相关结论。
At last, we establish systems of coincidence theorem and system of minimax theorems in G-convex under weaker assumptions. Our results generalize the corresponding results in recent literature.
我们对这类算法的收敛速率做了估计，最后还给出了其单调收敛定理。
We estimate the convergence rate of this method and give its monotone convergence theorems.
最后证明了模糊测度空间的正则扩张及表示定理。
The regular extension and representation theorems of fuzzy measure Spaces will be proved.
最后，利用择一性定理，获得了含不等式和等式约束的广义次似凸集值映射向量最优化问题的最优性条件。
Finally, the optimality conditions for vector optimization problems with set valued maps with equality and inequality constraints are obtained with it.
最后对均匀杂交算子给出了在模式的存活和模式的新建共同作用下的模式定理，这一结果深入和推广了目前模式定理的结果。
At last, the schema theorem for uniform crossover operator is proposed considering both schema survival and schema construction. It deepens and generalizes the existing results on schema theorems.
最后，我们介绍了风险理论中的极限定理。
最后讨论了向量值正规模糊子群与向量值模糊商群的性质，同时建立了向量值模糊商群的同构定理。
Some properties of VV normal fuzzy subgroup and VV fuzzy quotient group are discussed and the isomorphism theorem of VV fuzzy quotient group is established.
最后探讨了拉格朗日中值定理证明中辅助函数的构造方法，以此拓展对定理证明的思路。
Finally discusses the Lagrange mean value theorem proof method of constructing auxiliary function in order to expand on the idea of theorem proving.
特勒根定理2是电路理论中的重要定理。本文用矩阵方法分析其共轭性，最后给出特勒根定理完整的矩阵表述。
Tellegen's theorem 2 is important in circuit theory. This paper analyzes its conjugation with matrix method and represents the complete matrix formulation of Tellegen's theorem.
本文最后通过一个具体实例说明了乘积构形良划分性定理的几何意义。
Finally we illustrate the geometric significance of the theorem through a concrete example.
最后，结合拉格朗日微分中值定理改进了积分中值定理的条件和结论。
Finally, the condition and result of integral mean-value theorem are also improved combined with the Lagrange mean value theorem of differentials.
最后提出一种基于公钥密码和中国剩余定理的动态口令验证方案，进行方案安全性及性能的分析。
A dynamic password authentication which is based on the Chinese remainder theorem and RSA public-key cryptography are represented. Finally analysis is made on the scheme security and feasibility.
最后，针对学者提出的“部分分布决定性定理(PD定理)”，本文论证了其推理过程中存在的问题，说明该定理是错误的。
Lastly, this paper finds a mistake in a theorem named deterministic theorem of the partial distribution (PD theorem) by its author, and explains the reason.
通过建立二次型与对称双线性函数之间的对应关系，在双线性函数的概念下讨论二次型化标准型的问题，最后给出惯性定理的一个证明。
In this paper, we use the theory of symmetric bilinear function to solve problems of quadratic form, and finally give a proof of the inertia theorem.
类比关于数式的二项式定理，导出关于矩阵的二项式定理，并用数学归纳法予以证明，最后举例说明关于矩阵的二项式定理的应用。
Compared to digital binomial function, matrix binomial function is derived and proved with induction method and the application of it is illustrated with examples.
主要结果有定理1、2。而几何抽样模型下的结论是本文的特例，最后附有实例。
The main results are listed as theorems 1 and 2, while the conclusion. obtained from a geometric sampling model may be seen as a special case. Examples are given finally.
通过对两个求导例题的分析，归纳出求函数在某些特殊点导数的新的解法定理及推论，并举例说明它们的应用，最后对此求导方法进行总结。
Through analyzing two examples of seeking derivative, new theorem and deduction for solution to some functions derivation under the special condition are concluded, and examples are shown.
最后证明基于双重随机样本的统计学习理论的关键定理并讨论学习过程一致收敛速度的界。
Finally the key theorem of statistical learning theory based on random rough samples is proved, and the bounds on the rate of uniform convergence of learning process are discussed.
最后证明基于双重随机样本的统计学习理论的关键定理并讨论学习过程一致收敛速度的界。
Finally the key theorem of statistical learning theory based on random rough samples is proved, and the bounds on the rate of uniform convergence of learning process are discussed.
应用推荐