三种生成神经网络拓扑结构的方法在股票、商品价格预测中的应用及结果比较

对比了三种不同神经网络模型的生成方式:传统神经网络生成模型,遗传算法训练神经网络模型,以及在第二种方式训练参数的基础上,再使用传统神经网络优化生成模型.论文使用上述三种方法对代表性股票和商品价格进行拟合并预测,通过预测结果准确性和稳定性的比较发现:引入遗传算法后的神经网络在样本内的拟合误差有所降低,而第三种方法在样本外有最低的预测误差和最优稳定性.     

非均质气藏水平井三维渗流的产能预测方法

建立了非均质气藏水平井三维渗流产能预测的数学模型,采用有限元方法对其进行求解,求解时将地质模型区块中不同空间位置处的渗透率值以及其它气藏物性参数分别布置到有限元模型的相应位置处的网格中,从而体现了三维空间中气藏的非均质特性.分析了非均质性对气藏水平井产能的影响.结果表明,气藏的非均质性对水平井的产气量影响很大,在相同的生产压差下,存在高渗透带的气藏水平井产量明显高于均质气藏水平井的产量;高渗透带条数越多、渗透率越大,导致气藏内压力消耗越小,水平井产量越高.最后,结合松辽盆地徐深层气田的地质特点和储层特性,给出了该气田的水平井产能预测实例.研究方法符合气藏的实际情况,为气藏水平井,特别是非均质气藏水平井的产能预测提供了一种行之有效的方法.     

短时交通流混沌预测模型的比较研究

短时交通流预测是实现交通流诱导的关键技术之一.针对目前短时交通混沌预测模型预测结果差异较大的问题,归纳了4种基于混沌理论的短时交通流预测模型:RBF神经网络模型、最大Lyapunov指数模型、局域线性模型和Volterra滤波器自适应预测模型,并对这4种预测模型进行了比较研究.应用4种预测模型对几个典型的非线性系统进行预测,验证了算法的准确性.然后用这4种预测模型对微观实测交通流的时间序列进行实证分析.仿真结果表明,4种预测模型对典型混沌时间序列具有很好的预测效果;而对实测交通流预测,其预测精度和稳定性较差,但可以满足实时交通流预测的需要.     

基于Mamdani型模糊推理的震中烈度预测模型

对应用模糊推理进行系统预测进行了深入的研究,建立了以震级和震源深度为输入的基于Mamdani型模糊推理的震中烈度预测模型.并以四川地区震例数据为例,对数据信息提取,模糊规则建立等关键环节进行了详细的介绍,预测结果分析表明推理模型是可行和有效的.     

RBNS的线性预测模型

传统的准备金方法都是基于聚合数据的,聚合数据是个体数据的汇总,它们丢失了许多有用信息,影响了准备金预测的准确性.本文提出了一个基于个体数据的线性预测模型,该模型不需要对数据的矩的具体形式进行假设,更不需要对数据的分布进行假设,而只需假设个体索赔数据的前两阶矩存在,具有适用范围广,简单易操作等特点.在文章的最后,通过随机模拟把提出的方法与著名的链梯法进行了对比,模拟结果显示,本文提出的方法是行之有效的.     

Prediction error sampling procedure based on dominant Schur decomposition. Application to state estimation in high dimensional oceanic model

Iterative procedure is described to generate patterns of dominant Schur vectors of the system dynamics. Their roles in estimating the filter gain is study. These patterns are produced by several integrations of the model from a set of perturbations. This approach is motivated by a number of interesting results on stability of the filter whose gain is approximated in a subspace of dominant Schur vectors. A simple method for the filter design is presented which is aimed at overcoming the most serious drawback of advanced filtering algorithms for high dimensional systems related to very high computational cost in evaluation of the filter gain.The resulting filter will be compared with the existing ones, showing its relevance from a practical point of view. In order to demonstrate its efficiency, the new filter is tested on various experiments. These experiments include the much studied problem of estimating the solution of the Lorenz system as well as that of assimilating sea surface height observations in a high dimensional oceanic model. It is shown that significant increases in efficiency can be obtained by using this filter and that the proposed filter is very promising for solving realistic assimilation problems in meteorology and oceanography.     

Introducing a modified gradient vector method for optimization of accident prediction non-linear functions

In this paper, a new optimization method has been proposed for accident prediction non-linear models. This has been achieved by eliminating the Hessian matrix from the equation of optimal pace length in the gradient vector method. One advantage is that it is independent of the starting point in optimization processes and it provides convergence at the highest top as well. This method has been tested on an accident prediction model and its preference over the gradient vector method has been proven.     

油田开发指标系统的概率模拟预测

基于油田开发指标系统的复杂性和其影响因素带有明显的随机特征,从理论和实用上研究了油田开发指标系统的不确定性预测方法.方法提出采用神经网络建立开发指标和影响因素的关联关系;利用微分模拟研究开发指标的影响因素的概率分布;使用蒙特卡罗方法对影响因素进行随机抽样;依据得到的关联关系和影响因素的随机样本建立油田开发指标的概率预测模型.此外,设计了概率模拟器.实例研究表明开发指标的预测值以某概率落在某个区间内,这可为决策者做出符合实际的决策和准备应急决策方案提供依据.     

Validation of a complex urban noise model close to a road

The adequacy of a model for the sound level close to a road is investigated by comparing resulting predictions for the sound level over a building façade with measurements. The road model involves the road geometry (the number and positions of traffic lanes), the traffic structure (vehicle flow rates and their average speeds in each lane) and equivalent omnidirectional point sources representing the vehicles. It is found that the assumed road traffic noise source model is adequate only for predicting levels over the higher part of the façade. However the investigation has allowed definition of what improvements are needed in the road source modelling to enable adequate predictions over the whole of the building façade.     

Modeling stock index returns by means of partial least‐squares methods: An out‐of‐sample analysis for three stock markets

We analyze the underlying economic forces of the stock markets in Germany, the U.K. and the U.S. Identifying a number of variables evincing return predictability, we follow a partial least‐squares (PLS) approach to combine these observables into a few latent factors. Conditional on European markets, our findings indicate (i) superior prediction performance of PLS‐based schemes in comparison with both, a random walk and a first‐order autoregressive benchmark model, (ii) consistent profitable trading on the German and British market, (iii) profitable linear forecast combinations, (iv) the U.S. stock market is diagnosed as informationally efficient. Copyright © 2010 John Wiley & Sons, Ltd.