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.
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.
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.
混沌经济时间序列的预测方法研究是混沌经济非线性动力系统的重要内容。
The research on forecasting method of chaotic economic time series is the important part of the nonlinear chaotic economic dynamic systems.
根据股票市场是非线性动力系统的假设，利用混沌理论对混沌时间序列的分析方法，提出了股票价格预测方法。
A method of stock price prediction is presented by hypothesis of stock market being non-linear dynamic system and analyzing method of chaos theory for chaos time series in this paper.
这些非线性信息的提取，将进一步揭示径流时间序列的变化规律，并将对提高预测水平产生影响。
Theses nonlinear information can reveal the regularity for the annual flow time series and advance the prediction level.
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.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
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.
基于该网络的时间序列预测模型可以实现性能优越的非线性预报器，将其应用于非线性系统的故障预报能够取得良好的效果。
The LMBP neural network can predict nonlinear time series very well and the new method is effective for the fault prediction of nonlinear systems.
然而石油期货价格具有时间序列数据的典型特点，即非线性和非平稳性，这给价格的预测带来了极大的困难。
However, the oil futures prices involve the typical characteristics of time series data, nonlinearity and nonstationarity, which brings insuperable difficulties in the price forecasts.
应用基于邻近点的非线性自适应预测模型验证了观测时间序列存在数据缺损时的预测效果，并提出了相应的解决办法。
We exploit the above prediction model validate effect while there are omitted data in observed time series and propose the corresponding resolve.
基于混沌动力系统的相空间延迟坐标重构，利用混沌序列固有的确定性和非线性，提出了用于混沌时间序列预测的一种少参数非线性自适应滤波预测模型。
Based on the deterministic and nonlinear characterization of the chaotic signals, a new reduced parameter nonlinear adaptive filter is proposed to make adaptive predictions of chaotic time series.
但是混沌系统是由非线性动力机制决定的确定性系统，貌似随机运动的混沌系统内部存在确定性规律，所以混沌时间序列是短期可预测的。
But chaos is a deterministic system determined by the nonlinear dynamical mechanism. There is a deterministic rule in the interior of the chaotic system which is seemed as a random move.
计算结果表明，对于岩土体工程中的一维监测数据，通过非线性时间序列分析方法可以对其进行预测分析，该方法具有较高的实用价值。
The result shows that, based on the one-dimensional monitoring data, the displacement can be predicted by the method of nonlinear time series, and the method has practical value.
针对故障率时间序列的非线性与非平稳特性，提出一种基于支持向量经验模态分解(SVEMD)的预测方法。
A prediction method based on support vector empirical mode decomposition (SVEMD) is proposed to deal with the non-linearity and non-stationarity of failure rate data.
ESN(回声状态网络)是一种新型的递归神经网络，可有效处理非线性系统辨识以及混沌时间序列预测问题。
As a new type of recurrent neural network, echo state network (ESN) is applied to nonlinear system identification and chaotic time series prediction.
根据滑坡位移时间序列的非线性性质，应用混纯时间序列预测方法，建立滑坡预测的非线性混纯模型。
According to the nonlinear characteristics of landslide displacement time series, the nonlinear chaotic model is presented applying the forecasting method of chaotic time series.
股票市场是一个复杂的非线性动态系统，利用传统的时间序列预测技术预测效果不理想。
As stock market is a kind of complex non-linear dynamic system, the prediction results of traditional prediction technology are unsatisfactory.
股票市场是一个复杂的非线性动态系统，利用传统的时间序列预测技术很难反映市场变化的多因素，非线性、时变性等特点。
Stock market is a complex non-linear dynamic system. It is difficult to reflect market with the trait of more factors, non-linear and time variety using the traditional timing prediction technology.
股票市场是一个复杂的非线性动态系统，利用传统的时间序列预测技术很难反映市场变化的多因素，非线性、时变性等特点。
Stock market is a complex non-linear dynamic system. It is difficult to reflect market with the trait of more factors, non-linear and time variety using the traditional timing prediction technology.
应用推荐