至今已经提出了决策树的很多算法，通过分析已知的分类信息得到一个预测模型。
So far, there are many algorithms have been given and we can gain a prediction model by analyzed known catalog information.
实验结果表明，应用GP决策树算法能够正确完成对趋势预测模型的选择。
Experimental results show that the choice for trend forecasting models can be correctly finished by using GP-decision tree algorithm.
模型预测控制作为一种新型计算机控制算法，在工业控制界取得许多成功应用，现已成为一种重要的先进控制策略。
As a computer control algorithm, Model Predictive Control (MFC) has many successful applications in industry control process and has become an important advanced process control strategy.
电力系统电压暂态稳定在线预测分析的关键是寻找一种算法简单，精度高，预测时间长和鲁棒性好的预测模型。
The key problem of bus voltage predicted analysis on line is to research the predicted model, which includes simple algorithm, high accuracy degree, long predicted time and good robustness.
提出了原有模型的改进算法，从而提高了模型的有效性，并取得了比较好的预测结果。
It also gives the improved algorithm of the raw model, which makes the model be more efficient, and gets the good forecasting result.
本文提出基于新的激励函数BP算法建立误差预测模型，修正新型广义预测算法的预测输出。
In the paper presents the predictive out of a new generalized predictive Control is corrected by the error predictive model based on a new excite function BP arithmetic.
本文用已建立的发动机神经网络怠速模型，并采用DMC预测控制算法，完成了怠速控制的仿真研究。
By using the founded neural network model and DMC predict control algorithm, simulation research has been completed on the idle speed control in this pape.
该文介绍了一种基于人工神经网络的软件失效预测模型，给出了基于反向传播算法的多层前向网络的网络结构。
This paper presents a kind of software faults prediction model based on artificial neural network and the structure of the feed-forward multi-layer network with backpropagation learning algorithm.
运用改进的BP算法，建立了水泥强度预测模型。
Based on optimized BP algorithms, some prediction models are developed for cement strength.
针对化工过程某些非线性系统的不对称动态特性，提出了一种基于自校正模型的多模型预测控制算法。
To handle the unsymmetrical dynamic characteristics of some nonlinear systems in chemical process, a multi-model predictive control method was proposed based on self-tuning model.
本文中提出了基于ARMAX模型的新型广义预测控制，揭示了其控制策略与模型算法控制(MAC)之间的内在联系。
In this paper, a new type of generalized predictive control based on ARMAX model is proposed. The internal relations between the control strategy and model Algorithmic control (MAC) are revealed.
并且对基于神经网络的组合预测方法进行了研究，提出了一个神经网络和指数平滑模型组合运用的预测算法。
After study of the combined forecasting methods based on the ANN theory, it is put foreword that Exponential-Smooth (ES) and ANN combine a new prediction algorithm.
探讨了基于最大熵原理的公共交通需求预测模型的原理与算法。
The article explains the principle and algorism of the forecast model of public transport demand based on the maximum entropy principle.
对时序数据建模与辨识技术进行了分析，提出了使用鲁棒LS-SVM算法建立ARMA时序预测模型。
Time series modeling and identification techniques were analyzed and the ARMA time series model based on robust LS-SVM algorithm was proposed.
模型算法控制(MAC)是预测控制的一种，在惯性迟延对象的控制中有很好的应用。
MAC is one kind of predictive control methods, and has found many applications in controlling objects with a big inertia and delay.
收益管理本质上是一种前馈管理，这决定了预测模型和算法始终是收益管理的基础。
The management of the income is a kind of feed forward in essence, this determines that the model and the algorithm are foundations of management of incomes all the time.
提出了一种利用神经网络BP算法模型于发现和预测商业市场价格变化趋势的模型。
This paper puts forward a model of discovering and forecasting price trend in market, based on neural networks BP algorithms.
其次，根据混沌特性中的短时可预测性介绍了局部线性拟合和AR模型预测算法。
Secondly, the locally linear prediction and ar model is introduced for the chaos sequences have the character of local predictability.
基于改进灰色模型预测精度的思想，给出了一种改进的灰色模型预测算法。
Based on ideals of improving on forecasting precision of the grey model, an improved grey forecasting algorithm was given.
采用单神经元PI控制算法与神经网络预测模型相结合的控制策略，用PI控制规律来确定控制器的输出。
The control strategy unites single neuron PI control algorithm and the predictive model based on neural network. The output of the controller is determined by PI algorithm.
然后应用检测与估值理论提出一种支持这一模型的自适应预测切换算法。
An adaptive decision vertical handoff algorithm using the detection and estimation theory to support this model is provided.
然后应用检测与估值理论提出一种支持这一模型的自适应预测切换算法。
An adaptive decision vertical handoff algorithm using the detection and estimation theory to support this model is provided.
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