RA码具有线性时间编码算法。
所谓线性时间逻辑仅限于这种类型的推理。
So called linear time logics are restricted to this type of reasoning.
所有的耗时部分系统的并行和大约线性时间。
All of the time consuming parts of the system are parallelize and roughly linear time.
引入了非线性时间序列的局部投影消噪算法。
A local projective noise reduction for nonlinear time series is here introduced.
门限自回归模型是一种新近创立的非线性时间序列摸型。
The threshold autoregressive model is a kind of non-linear time series model recently established.
提出单输入非线性时间最优开关控制的非线性规划算法。
A nonlinear programming method for time optimal switching control with a single control input is proposed.
识别混沌是对非线性时间序列进行分析、预测、控制的基础。
How to identify chaos is the foundation of analysis, prediction and control of nonlinear time series.
你们会感到很迷惑，但吉萨金字塔在线性时间上讲并不是最早的。
This will be confusing to you, but the Giza Complex has no true beginning point in linear time.
提出了一种基于相重构和主流形识别的非线性时间序列降噪方法。
A noise reduction method in nonlinear time series based on phase reconstruction and manifold learning was proposed.
当这个事件在线性时间产生，人类将依据这两种适应性模式被极化。
By the linear time this event takes place, humanity will be polarized according to these two adaptive patterns.
机械时间与人类时间的并置以及非线性时间都产生了印象主义效果。
Juxtaposition of mechanical time and human time, together with achronological time display the impressionist effect.
从地球的线性时间来看，现在的阶段正是独处和自我审视的绝佳时机。
The present phase of linear time on your planet is a uniquely opportune juncture for self-review and chosen solitude.
针对神经网络的特点，探讨了神经网络对非线性时间序列预测的应用。
Based on specific features of the neural network, this paper is concerned with its application to prediction of nonlinear time sequence.
这一直是他的前提，这是由于他对他所理解的线性时间去如何把握造成的。
This has been his preconditioning and is caused by his grasp of how to deal with a time that he understands as a linear sequence.
事实上，必须改变过去以防止能量和创伤因过度伤痛而延伸到线性时间的现在和未来。
Indeed the past must be changed to prevent the energy and trauma from over-bleeding into the present and future, in linear terms.
时间序列的非线性检测对于非线性时间序列分析、混沌特性研究有着重要意义。
Test of nonlinearity of time series is very important for nonlinear time series analysis and study of chaotic dynamics.
他们不受线性时间的约束，并且能够在时间中向前或向后移动来改变你们的经历。
They are not bound by linear time, and can go backwards or forwards in it to alter your experience.
最后，设计模拟实验，探讨有关神经网络的线性时间序列预测方面的问题，得出结论。
Finally, We designed a pseudo experiment to talk about the linear time series analysis based on neural networks theory.
至于时间概念，这已经强迫你们生活的，控制你们在线性时间状态的，将被彻底的废除。
Time, which has constrained your lives and kept you in line, will be abolished.
利用非线性时间序列分析方法，以汉语语言认知为例，探讨语言认知系统的动力学特性。
By using the method of nonlinear time series analysis and taking the Chinese language cognition as an example, the dynamic characteristics of language cognition system are discussed.
基于在图中寻找强连接节点的算法，给出一种线性时间复杂度算法来检测故障树中的模块。
Based on the algorithm to find strongly connected nodes of a graph, this paper presents a linear time algorithm to detect modules of a fault tree.
该方法关于数据库的大小、属性个数具有近似线性时间复杂度，这使得算法具有好的扩展性。
The method has the nearly linear time complexity with the size of dataset and the number of attributes. This results in good scalability.
本文认为，诗人否定了循环时间和线性时间，但肯定了作为“瞬时性”与“当下性”的时间。
In the authors opinion, the poet denied the circulating time and linear time, while affirm the instantaneous time and current time.
Sequitur压缩算法是线性时间在线算法，为给定的字符串输入生成了一种与上下文无关的语法。
The Sequitur compression algorithm is a linear-time online algorithm that forms a context-free grammar for a given string input.
本文提出了一种改进的细分嵌入算法——端点外接圆法(ETCM)，该算法具有线性时间复杂度。
An improved algorithm of node refinement scheme called endpoint triangle's circumcircle method (ETCM) is proposed, which has a linear time complexity.
他认为当前的空间是无方向的、无深度的，它不仅仅对立于时间，而且还被定义为线性时间的消失。
He thinks the present space is directionless, has no depth, which isn't merely against time, and is still defined the disappearance of linear time.
由于这些变量具有非线性时间序列数据，用人工神经网络(ANN)将使用反向传播算法作为学习算法。
Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm.
由于这些变量具有非线性时间序列数据，用人工神经网络(ANN)将使用反向传播算法作为学习算法。
Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm.
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