构造了一类非线性序列生成器，其生成的序列周期长，线性复杂度高，且可控制。
A class of nonlinear sequence generators of long period and high linear complexity are constructed.
建立了一个时间序列的门限自回归的预测模型，为股票市场的非线性研究这一前沿领域作了一点新的尝试。
Set up one time door limit prediction model of autoregression of array, study for nonlinearity of stock market this front field make new try a bit.
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.
利用目标的方位序列跟踪目标的运动参数是非线性领域的一个经典问题。
It is a classic problem to estimate motion-parameter of target based on bearing series in non-linear domain.
其次，对网络时延(RTT)特性进行了分析，利用线性和非线性方法对从互联网上采集的RTT时间序列进行统计分析。
Next, the thesis analysis the characterization of Roundtrip time delay (RTT). The RTT time series collected from the Internet are studied statistically by using both linear and nonlinear methods.
采用延迟坐标状态空间这种相空间重构方法，对非线性系统中的单一时间序列进行分析，从中恢复出系统内部存在的非线性动力学特性。
The delay-coordinate method is adopted to reconstructed the space phase, and to analyze the single time series in the non-linear systems and resume the nonlinear kinetics characteristics.
文章通过对一套市场价格预测模型体系的介绍，综合运用时间序列模型、多元非线性回归和组合模型来预测市场价格走势，探索从多角度综合预测市场价格的问题。
In this paper, a new model system is introduced, which synthetically applies time series model, nonlinear regression and combination forecasting model to forecast the change of the market price.
本文主要研究非线性动力学方法在时间序列分析中的应用。
This paper focuses on the application of nonlinear dynamical methods in the analysis of time series.
由于股票预测是不确定、非线性、非平稳的时间序列问题，传统的方法往往难以取得满意的预测效果。
Because stock forecasting is a uncertain, nonlinear and nonstationary time series problem, it is difficult to achieve a satisfying prediction effect by traditional methods.
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.
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.
相关维数是定量描述非线性时间序列的一个重要参数，在脑电、心电等生物医学信号的特征描述方面得到了广泛地应用。
Correlation dimension is an important parameter to measure a nonlinear time sequence quantitatively, and it is widely used to analyze biomedical signal, such as EEG and ECG.
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.
得到的最优控制律由解析的线性前馈-反馈项和伴随向量序列极限形式的非线性补偿项组成。
The obtained optimal control law consists of analytical linear feedforward and feedback terms and a nonlinear compensation term which is the limit of the adjoint vector sequence.
特别针对模型未知的非线性系统，研究了时间序列分析和神经网络相结合的故障预报方法。
The methods, which combine time series analysis and neural networks, are especially studied and applied in the model-unknown nonlinear system.
结合时空系统机制和历史资料的分析，建立非线性时空序列预测理论与方法。
Simultaneously, the forecast theory and method of nonlinear time series is established, which combines mechanism of the time space system with analyzing historical data.
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.
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.
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.
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.
非线性理论在刻画金融时间序列的波动方面有着非常重要的作用。
The non-linear theory has been playing an important role in describing volatility of financial time series.
混沌和支持向量机理论为研究复杂多变的非线性水文时间序列开辟了新的途径。
Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series.
本文将讨论综合运用非线性回归模型和时间序列分析的方法进行变形预报。
This article demonstrates that deformation forecast will be performed by a comprehensive method of non linear regression model combined with time series analysis.
提出了一类用于非线性时间序列建模的混合自回归滑动平均模型(MARMA)。
A mixed autoregressive moving average (MARMA) model is proposed for modeling nonlinear time series.
提出了一种非线性时间序列混沌特征的自动提取方法。
A new method is put forward, which can implement auto-extracting of chaos features of nonlinear time series.
设计了模型结构和参数分别进化，共同识别方案，实现对非线性时间序列分析模型结构和参数进行全局最优搜索。
A new stepped evolutionary scheme is designed to search the global optimal structure and parameters of the nonlinear time series model.
非线性组合序列作为一类重要的密钥流生成器，其设计和分析一直是序列密码研究的一个重要方向。
Nonlinear combined sequences as a kind of the important key sequence generators, its design and analysis are always hotspot and difficulty of stream cipher.
从信息论角度出发，利用神经网络非线性时间序列预测模型，构造了油田产油量、产水量的多维时间序列神经网络预测器。
In this paper, using neuron network models of nonlinear multidimensional time series prediction, neuron network predictors for the oil production and water production of oil fields were constructed.
该文介绍了内回归神经网络逼近非线性ARMA模型、用于时间序列预测的可行性。
This paper introduces the feasibility of inner recursion networks using in non-linear ARMA model approaching and time series forecasting.
该文介绍了内回归神经网络逼近非线性ARMA模型、用于时间序列预测的可行性。
This paper introduces the feasibility of inner recursion networks using in non-linear ARMA model approaching and time series forecasting.
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