要从客户端访问所有这些数据并进行处理或保存任何更改，在网络带宽和计算(用于数据序列化)方面将产生非常大的开销。
It would be very costly in terms of network bandwidth and computation (for data serialization) to access all this data from the client for processing and then persist any changes.
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.
其次，实验中测得了大量的混沌数据，在神经网络模型的启发下提出了一种新的符号序列去噪算法，应用该算法提高了测量精度。
Secondly, we have obtained plenty of chaotic data, and presented a new method derived from Neural Network theory to process the symbolic series, which improves the accuracy of measurement.
根据大坝监测数据在时序上变化特征，应用了神经网络和基于遗传算法的时间序列的非线性预测模型。
Founded on change speciality of series of dam safety monitoring forecast, artificial neural networks and nonlinear models of time series based on genetic algorithms are applied.
有关序列、结构、通路和基因与基因产物网络的一系列广泛的数据对生物学和生物医学研究中的假设检验和发现是有用的。
A wide range of data on sequences, structures, pathways, and networks of genes and gene products is available for hypothesis testing and discovery in biological and biomedical research.
其思想是通过将网络审计数据转化为时序数据库，对其进行序列模式挖掘以提炼出用户行为模式，并由此进行异常检测。
The idea is to transform the net audit data into time series database and mine the sequence pattern to extract the user behavior pattern , and then to use behavior pattern in anomaly detection.
金融时间序列具有很强的随机性和非线性性，而神经网络具有良好的非线性映射能力及自适应、自学习和良好的泛化能力，因此非常适合处理金融时间序列这样的数据。
Financial time series has high randomicity and nonlinearity. Neural network is quite suitable in the process of financial time series data for its good ability of nonlinear mapping and generalization.
最后，我们提出了结合时间序列表达数据和静态数据来构建动态调控网络的方法。
Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks.
采用BP网络对不平稳时间序列进行数据拟合，处理趋势部分，利用ARMA模型处理随机部分。
The trend part of the data can be fitted with BP (back propagation) neural network and the random part is processed by a normal ARMA (auto regressive moving average) model.
MGI能够通过各种方法访问，包括基于网络的搜索方式，一个基因组序列浏览器和可下载的数据库报告。
MGI can be accessed by a variety of methods including web-based search forms, a genome sequence browser and downloadable database reports.
指派数据包到多个序列之一的过程，基于类别，对于通过网络的优先级处理叫做调度。
The process of assigning packets to one of multiple queues, based on classification, for priority treatment through the network is called scheduling.
指派数据包到多个序列之一的过程，基于类别，对于通过网络的优先级处理叫做调度。
The process of assigning packets to one of multiple queues, based on classification, for priority treatment through the network is called scheduling.
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