查询词典 prediction

This scheme was composed of two parts: intra prediction and inter prediction. For the intra prediction part, 8×8 block DCT-HT transform-domain converse was used, and then intra mode decision was done in HT domain to obtain the optimum mode, so that good intra prediction was acquired. For the inter prediction part, Motion Vector got from Variable Length Decoding in MPEG-2 stream was reused as Prediction Motion Vector of H.264, then motion prediction mode and search region were reset, so that better prediction result was acquired.

该方案中，帧内预测使用了8×8块的DCT-HT的变换域转换，然后在HT域进行Intra模式判决得出最优模式，从而得到理想的帧内预测；帧间预测重用了MPEG-2码流中变字长解码得到的运动矢量MV，作为H.264的PMV，通过重新设定运动预测模式以及搜索范围，从而获得较好的预测效果。

Among used machine learning methods, the gradient descent method is widely used to train various classifiers, such as Back-propagation neural network and linear text classifier. However, the gradient descent method is easily trapped into a local minimum and slowly converges. Thus, this study presents a gradient forecasting search method based on prediction methods to enhance the performance of the gradient descent method in order to develop a more efficient and precise machine learning method for Web mining.However, a prediction method with few sample data items and precise forecasting ability is a key issue to the gradient forecasting search method. Applying statistic-based prediction methods to implement GFSM is unsuitable because they require a large number of data items to model a prediction model. In the contrast with statistic-based prediction methods, GM(1,1) grey prediction model does not need a large number of data items to build a prediction model, and it has low computational load. However, the original GM(1,1) grey prediction model uses a mathematical hypothesis and approximation to transform a continuous differential equation into a discrete difference equation in order to model a forecasting model.

其中梯度法是一个最常被使用来实现机器学习的方法之一，然而梯度法具有学习速度慢以及容易陷入局部最佳解的缺点，因此，本研究提出一个梯度预测搜寻法则(gradient forecasting search method， GFSM)来改善传统梯度法的缺点，用来提升一些以梯度学习法则为基础的分类器在资讯探勘上的效率与正确性；而一个所需资料量少、计算复杂度低且精确的预测模型是梯度预测搜寻法能否有效进行最佳解搜寻之关键因素，传统统计为基础之预测方法的缺点是需要较大量的数据进行预测，因此计算复杂度高，灰色预测模型具有建模资料少且计算复杂度低等优点，然而灰色预测理论以连续之微分方程式为基础，并且透过一些数学上的假设与近似，将连续之微分方程式转换成离散之差分方程式来对离散型资料进行建模及预测，这样的作法不尽合理，且缺乏数学理论上的完备性，因为在转换过程中已经造成建模上的误差，且建模过程仅考虑相邻的两个资料点关系，无法正确反应数列未来的变化趋势。

With the IAP Prediction System of Short-term Climate Anomaly, two sets of seasonal and extra-seasonal ensemble hindcasts have been performed during the period of 1980～1994 in order to assess the prediction skill of the IAP PSSCA with two different schemes The difference of the two schemes is in the different version of AGCM used, with the IAP AGCM 1 1 in one scheme and the IAP AGCM 1 2 with an improved surface albedo parameterization in the other Comparisons of the hindcast results with the observations show that IAP PSSCA is capable of predicting the precipitation anomaly to some extent In the eastern China largely affected by Asian monsoon and SST anomalies, the prediction skill is relatively high, especially in the Southeastern China where the anomaly correlation coefficient can reach as high as 0 50 in the severe flood and drought years This may suggest that the possible mechanisms for these severe disasters have been well captured by the IAP PSSCA The prediction skill is higher for the modified model AGCM 1 2 as the model's climatological state is well simulated This indicates that models with reasonable land process will improve the prediction skill for short-term climate prediction

利用中国科学院大气物理研究所研制的短期气候距平数值预测系统，种版本的大气环流模式：AGCM 1.1和AGCM 1.2，分别以2月11～19日的9天大气观测值为初始场，以给定海温为边界场，对1980～1994年的15年的降水异常进行了两组集合后报试验。对试验结果进行定量评估表明：IAP PSSCA对降水异常具有一定的预测能力，特别是在中国东部受东亚季风及海温异常影响的地区，IAP PSSCA具有较高的预报技巧，其中以东南区域(包括江淮流域和华南地区)最高，尤其是对有洪涝灾害的降水异常年，距平相关系数在0.50左右，接近可供业务使用的要求，说明模式能够抓住在东亚季风区存在的某种物理机制，从而提高了这一地区的预报技巧；另外，两个大气环流模式相比，改进了地表反照率的AGCM 1.2的15年集合平均预测技巧略高于AGCM 1.1，特别是在华北地区，预测效果有明显提高，这表明改进地表反照率从而改进了模式的气候平均态的模拟，能提高气候模式的预测能力，说明较好的陆面过程引入模式对短期气候预测是有益的。

The paper puts forward stock prediction pattern based on BP Neural Network, depending on Neural Networks approachability to non-linear function. Compared with the prediction method of Time Order, one of the traditional prediction methods, BP algorithm is verified to be feasible and effective on stock prediction.

本文借助神经网络对非线性函数的逼近能力，提出基于BP神经网络的股票价格预测模型，通过对比传统的时间序列预测方法，证实BP神经网络算法用于股票预测的可行性及有效性。

In order to improve the correct rate of enterprise's financial distress prediction and reduce the sample number and training time, renyi-entropy and least squares support vector machine is applied to the field of financial distress prediction on the basis of the traditional support vector machine prediction model and advances a kind of prediction model which is based on Renyi-entropy and LS-SVM.

为了提高企业财务困境预测的正确率，减少训练模型的样本数和训练时间，在传统支持向量机预测模型的基础上，将Renyi熵和最小二乘支持向量机算法应用于财务困境预测，提出了一种基于Renyi熵的最小二乘支持向量机预测模型。

Based on the research work of basic theories including phase space reconstruction,embedding theorem,correlation dimension,local dynamics,Lyapunov exponents,surrogate data etc,based on the research work of general methods such as principal component analysis,correlation dimension GP algorithm,false neighbors method,nonlinear time series prediction,local prediction, adaptive prediction,neural network model,support vector machines regression model, prediction power,nonlinear detection,coarse-graining methodology,conditional entropy and so on,the framework of nonlinear time series analysis are constructed.

在对包括相空间重构、嵌入定理、关联维数、局部动力学、Lyapunov指数、替代数据(来源：A97BC论文网www.abclunwen.com)、等基本理论与其物理意义的研究和讨论基础上；在对包括主分量分析、关联维数GP算法、伪邻近点法、非线性时间序列预测、局域预测、自适应预测、神经网络模型、支持向量回归模型、预测效果、非线性检测、粗粒化方法、条件熵等非线性时间序列一般分析方法的原理和算法研究基础上；构建了新的非线性时间序列分析的理论体系，归纳总结了非线性时间序列分析的基本问题和主要研究方面。2。

The speciality to let several coefficients of S-M algorithm are analyzed;(2) We first propose the nonlinear and adaptive prediction model based on several closest neighbors and extend the method to multistep prediction;(3) We first exploit Empirical Mode Decomposition technology to predict hopping frequency codes, it is namely that a complex time series is decomposed as several Intrinsic Mode Functions easy to prediction, then predict these IMF respectively, at last reconstruct estimated values of the time series;(4) We exploit the above prediction model validate effect while there are omitted data in observed time series and propose the corresponding resolve.

1具体分析了S-M算法中几个参数设置的特点；(2)首次提出了基于邻近点的非线性自适应预测模型，同时将这种算法推广到多步预测；(3)首次采用经验模态分解技术预测跳频码，即将一个复杂难以预测的时间序列分解为几个相对容易预测的本征函数，然后分别预测这些本征函数，最后重构出时间序列的估计值；(4)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。

The paper discusses the subjective and objective meaning of "almost"from connotation and prediction,analyses the semantic prediction and pragmatic prediction of the adverb,and expounds the negative meaning from the angle of prediction.

从蕴含和预设的角度对"差点儿"的客观意义和主观意义进行了分析，分析了"差点儿"的语义预设和语用预设，并从预设的角度解释了羡余否定现象的产生。

On this basis, according to historical data, apply ANN and differential simulation method to get the quantitatively correlative relations between each production and its own influence factors, and introduce the new methods of prediction for dynamic indexes with gas-field development (The combinatorial prediction method based on fuzzy comprehensive evaluation, the method of ANN to select optimally combinatorial prediction models and the ANN prediction method based on genetic algorithm).(2) Base on mathematical programming, combine with quantitative economics and techno-economics, introduce economical indexes to establish production"s distribution optimal model, production"s constitution optimal model and measured production"s constitution optimal model, including multi-objective models and five-years models. Upon this, the optimal project for all gas field and each gas-collected factory can be got. Also, introduce the time value of capitals to improve on these models.(3) Base on the optimal solution theory and algorithm theory for the nonlinear programming problem, introduce the SUMT algorithm and genetic algorithm to study how to solve the models, and on the basis of normal genetic algorithm, make use of auto-adaptively modulating method to improve on normal genetic algorithm; Base on algorithm"s convergence theory and calculation"s complexity theory to analyze seriatim SUMT algorithm"s convergence and genetic algorithms convergence, and compare performance with each other.

在此基础上，利用神经网络方法和微分模拟方法根据历史数据得到各分项产量与其影响因素之间的定量关联关系，并引入气田开发动态指标新的预测方法(基于模糊综合评判的组合预测方法、神经网络优选组合预测模型预测方法以及基于遗传优化的神经网络预测方法)；(2)以数学规划为基础，结合数量经济学和技术经济学，引入经济指标建立产量分配优化模型、产量构成优化模型、措施产量构成优化模型、气田开发多目标规划模型以及五年规划模型，进而获得全气田及各采气厂的最优方案，并引入资金时间价值对五年规划模型进行改进；(3)以非线性规划问题的最优解及算法理论为基础，引入SUMT算法以及遗传算法对模型的求解进行研究，并在原有的遗传算法基础上，引入自适应调整方法对遗传算法进行改进；以算法的收敛性理论和计算复杂性理论为基础，逐一分析SUMT算法以及遗传算法的收敛性，并比较三种算法的优劣性。

The phase space reconstruction theory of chaotic dynamic system, in combination of the non-linear reflecting and pan-capacity of neural network, can be used to establish the prediction model; and a kind of new prediction method is suggested to realize the complete tracing of phase point evolution process and to predict "the price nails", whereby improving the prediction accurateness and effectively solving the problem of negative prediction, with the satisfactory results obtained.

采用混沌理论预测系统边际电价针对我国电力市场电价变化特点，利用电价和负荷时间序列的混沌特性，重构准确的电价序列相空间，通过跟踪相空间相邻相点的演化趋势，建立基于快速BP网络的电价预测模型，对我国川渝电网电价进行预测，取得良好效果利用混沌动力系统的相空间重构理论，结合神经网络的非线性映射和泛化能力建立预测模型，提出一种新的预测方法实现了相点演化过程的全局跟踪，对"价格钉"进行预测，提高了预测精度，有效解决了负预测问题，得到满意的结果。

There are different types of personal loans available.

有许多不同类型的个人贷款优惠。

The above mentioned teaching model of spiral rising can be used to avoid rigidness in teaching.

上述螺旋式上升的教学模式，可以避免教学形成模式後的僵化现象。