递归定理

词组短语

recursion theorem

与递归定理相关的网络例句 [注：此内容来源于网络，仅供参考]

By transforming the delayed neural model to the describer model and then employing the Lyapunov-Krasovskii stability theorem, linear matrix inequality LMI technique, S procedure, and some algebraic inequality method, a new sufficient condition is derived, which is determined by the coefficients of the model and includes more tuning parameters for determining the globally asymptotical stability of recurrent neural networks with time-varying delay.

首先将要研究的模型转化为描述系统模型，然后利用Lyapunov-Krasovskii稳定性定理、线性矩阵不等式技术、S过程和代数不等式方法，得到了确保时变时滞递归神经网络渐近稳定性的新的充分条件，并将它应用于常时滞神经网络和时滞细胞神经网络模型，分别得到了相应的全局渐近稳定性条件。

If one makes some omissions in Chapter 4, for example, the proof of the theorem of Lindemann—Weierstrass, one is likely to have several weeks left after the completion of this material.

并且在讲授过程中可以不讲引言中有关递归定理的证明，第一章的有关自由群的小节，第二章和第三章的最后一节。

Secondly, the properties of output sequences of stop-and-go clocked combiners are discussed. It is proved that the output sequences are stationary and obey strong law of large numbers and central limit theorem. Using the probabilistic model properly built, we analyze the rate of coincidence between the output sequences of two kinds of generators with basic operations and corresponding LFSRs sequences. All the computational formulae are derived. A recursive algorithm for efficiently computing the poster probability of partial input strings and corresponding joint probability is introduced. Moreover, divide-and-conquer attacks on this kind of generators which reconstruct the initial states of the input stop-and-go generators individually are proposed.

其次，分析了一般钟控停走组合生成器输出序列的概率分布性质，证明了生成器的输出序列是严平稳的，且服从强大数定律和中心极限定理；考察了钟控加法型组合生成器和钟控乘法型组合生成器的输出序列和相应的常规钟控下线性移位寄存器序列之间的符合率，给出了符合率的具体计算公式；给出了组合器输出序列段和输入序列段之间的联合分布以及部分输入序列段的后验概率的有效递归算法，进一步提出了利用后验概率对钟控组合生成器进行分别征服相关攻击的方法。

First, the Lyapunov functional and variation of constants method are adopted to study the effect that Sigmoid function and the relation of resistance, capacitance and current in Hopfield neural networks have on the stability of networks. The stability criterion constructed by physics parameters is obtained. Thus how the constrained relation of physics parameters affects the stability of Hopfielf neural networks is clear. Based on the study above, the perturbation model of recurrent neural networks is constructed. And the theorems of the existence of solution of perturbation model are presented.

首先，采用Lyapunov泛函法和常数变易法研究Hopfield神经网络中给出的电阻、电容、电流之间的关系以及Sigmoid函数对网络稳定性的影响规律，得出仅由物理模型参数构成的稳定性判据，从而弄清物理模型参数约束关系对Hopfield神经网络稳定性所起的作用，在此基础上，构建了递归神经网络的扰动模型，并通过讨论扰动模型解的存在性问题，给出递归神经网络扰动模型解的存在性定理。

与递归定理相关的网络解释 [注：此内容来源于网络，仅供参考]

first mean value theorem：第一中值定理

第一类沃尔泰拉积分方程|Volterra integral equation of the first kind | 第一中值定理|first mean value theorem | 递归边图|recursive edge graph

recurrence theorem：循环定理

recurrence relation 递推关系 | recurrence theorem 循环定理 | recurrence time 递归时间

recurrence time：递归时间

recurrence theorem 循环定理 | recurrence time 递归时间 | recurrent 循环的

recursion of order p p：次递归式

recursion formula 递推公式 | recursion of order p p次递归式 | recursion theorem 递归定理

recursion theorem：递归定理

(注:严格来说我们要援用递归定理(Recursion Theorem)来保证以上的构作方法是妥当的,在此不赘. ]

general recursion theorem：一般递归定理

通用分时系统 general purpose time-sharing system | 一般递归定理 general recursion theorem | 通用递归应用与算法语言 general recursive applicative and algorithmic language

recursion theory：递归理论

recursion theorem 递归定理 | recursion theory 递归理论 | recursive algorithm 递归算法