提出了一种新的神经树模型来进行时间序列预测。
A new neural tree for modeling the time-series forecasting is proposed in the paper.
给出了基于径向基函数网络的混沌时间序列预测的方法。
A method based on radial basis function networks for forecasting chaotic time series is proposed.
本文提出了一种基于时间序列预测的延迟容忍网络路由算法。
A routing algorithm based on time series prediction for delay tolerant network is proposed.
提出了一种用于混沌时间序列预测的改进型加权一阶局域法。
This paper proposes an improved adding-weight one-rank local-region method for prediction of chaotic time series.
目的建立一种新的具有抗噪声能力的神经网络时间序列预测模型。
Aim To construct a new time-series forecasting model based on neural network with the capability of noise immunity.
采用新型多重分支时间延迟神经网络进行混沌时间序列预测研究。
A new multi-branch time delay neural network is adopted to conduct prediction research on chaotic time series.
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.
采用灰色系统理论对原地爆破浸出率的时间序列预测问题进行了研究。
In this paper, the theory of gray system is adopted to study the time series prediction of the leaching rate of in-situ blasting and leaching ore.
运用时间序列预测法对环渤海地区主要航线的客（车）运量进行了预测；
The prediction was made in the volume of ferry in main routes by Time Sequence Prediction method.
本文第一章介绍了该课题的背景意义以及时间序列预测的国内外研究现状；
Time series forecasting refers to the use of the historical observations of time series to predict the value at a future time.
目的：探讨ANN时间序列预测模型在疾病发病率或死亡率预测上的应用前景。
Objective: To explore the prospect of predicting disease incidence of the predictive model of nonlinear time series by BP neural network.
为了提高铁矿石消费量的预测精度，采用一种基于智能计算的时间序列预测方法。
In order to improve the prediction accuracy of iron ore consumption, using a time series forecasting method based on intelligent calculation.
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.
该模型具有计算简便的特点，而且具有可比性，能反映不同时间序列预测方法有效性。
These combination forecasting models are characterized with simple algorithm and comparison of forecasting effectiveness to different time series.
分析了基于记忆库混沌时间序列预测方法，引入一种改进核函数的支持向量机分类器。
Secondly the prediction technology of chaotic time series is studied based on memory-based predictor.
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.
这种用改进了的自组织方法所构成的GMDH型神经网络可以应用于混沌时间序列预测。
An improved GMDH-type neural network and its application to predicting chaotic time series are proposed.
预测结果表明，与传统的预测方法相比，混沌时间序列预测的精度和可信度得到了提高。
Forecast result indicate that comparing with traditional forecasting method, chaotic time series method can improve the precision and reliability of forecast result.
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.
这一概念是对线性偏自相关的一般化，由它可以得到度量时间序列预测复杂性的定量方法。
By means of it, we could get the quantitative method to measure the intrinsic prediction complexity of time series.
这一概念是对线性偏自相关的一般化，由它可以得到度量时间序列预测复杂性的定量方法。
The concept is the generalization of partial autocorrelation. By means of it, we could get the quantitative method to measure the intrinsic prediction complexity of time series.
通过长系列的降雨资料确定了水文年，通过考虑时间序列预测了未来一段时期的水文状况。
The hydrological year was determined by long series rainfall data and the hydrological regime was predicated by considering time series method.
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.
在水文预测中，应用混沌分析方法需解决相空间重构、时间序列的混沌性识别和混沌时间序列预测等关键技术。
In hydrological prediction, it is necessary that t he reconstruction of chaotics Tate space and the diagnosis of chaotic behavior as well as the chaotic pre diction are solved.
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.
针对定位传感器应用中相角和齿槽数信号异常的问题，利用硬件冗余和差分法进行时间序列预测，解决了该类问题。
Aiming at the abnormity of the phase and groove number, hardware redundancy and difference method is used to forecast time series and the problems are resolved.
移动平均法是一种时间序列预测法，当时间序列没有明显的趋势变动时，使用移动平均就能够准确地反映实际情况。
Moving average method is one of time series forecasting method, if time series have no apparent tendency moving, using moving average method can accurately reflect actual situation.
试验结果也证明，在求解函数拟合和时间序列预测等实际问题时，对比同类算法，UGEP算法体现出了较大的优越性。
The simulation results from function fitting and time series prediction indicate that UGEP performs better than other similar algorithms in each of experimental.
试验结果也证明，在求解函数拟合和时间序列预测等实际问题时，对比同类算法，UGEP算法体现出了较大的优越性。
The simulation results from function fitting and time series prediction indicate that UGEP performs better than other similar algorithms in each of experimental.
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