变步长LMS算法相空间重构的AR模型预报仿真

研究船舶运动姿态预报问题,存在着预报精度不高和收敛速度慢的问题,根据船舶运动具有混沌特性的特点,提出了VSS-LMS算法重构相空间,建立AR预报模型,实现了对船舶运动姿态的预报仿真,提高了稳态的预报精度,并在初期就能很好地提高收敛速度。经实验证明,基于VSS-LMS算法的混沌相空间重构AR预报模型预报精度更高、预报时间更长,且具有更快的收敛速度,为船舶运动姿态实时在线预报提供了理论依据。     

融合数值模式预报数据的深度学习PM2.5浓度预测模型

PM2.5污染问题是中国近年来引起广泛关注的环境问题,对PM2.5浓度进行预报有重要意义.传统的预报方法是基于空气动力学理论的数值模式预报方法.最近几年深度学习方法被广泛应用于PM2.5浓度预报问题.之前的深度学习预报方法主要是使用观测站的观测数据建立单点式的预报模型.本文使用ConvLSTM深度神经网络建立模型,在中国及周边区域的PM2.5数据集上实现了网格化的序列到序列预报.模型通过卷积模块提取空间特征,通过LSTM模块提取时间特征,适合解决PM2.5网格化预报问题.同时,模型中使用了再分析数据和模式数据两种不同来源的数据结合起来进行预报,融合了深度学习方法和传统数值模式方法.实验表明,模型的均方根误差比数值模式预报下降30.2%,具有良好的预报效果.     

新型自适应模型算法控制算式

本文分析了模型算法控制输出预报存在的问题,提出一种精度高、收敛性好的输出自适应预报方法,给出了具有良好自适应功能和稳定性的自适应模型算法控制算式。文中对新型预报和控制算式进行了仿真验证,获得了满意的结果。     

太阳质子事件短期预报模型研究

太阳质子事件预报是空间天气预报的重要组成部分.在太阳活动预报研究中,针对开发预报方法研究问题,已有的太阳质子事件预报模型主要采用统计和神经网络的方法.为了提高预报精度和准确率,选用了支持向量机和K近邻相结合的方法(称为SVM-KNN方法)建立太阳质子事件预报模型.模型选择的预报因子除了已有质子事件预报模型选用的传统太阳活动区黑子特征参量,还加入太阳活动区磁场参量.仿真预报采用2002年和2004年的数据,结果证明采用预报模型具有较高的报准率,证明SVM-KNN方法是一种有效的太阳活动预报方法.     

人工神经网络在小区域强降水预报应用研究

针对小区域强降水的非线性性质,利用T213数值预报产品,通过人工神经网络建模方法进行预报释用,对数量众多预报因子采用经验正交分解方法,浓缩大量因子的有效信息,建立逐日小区域强降水的人工神经网络预报模型.运用与实际业务预报相同的方法进行逐日预报试验,并与回归预报模型进行比较.结果表明,人工神经网络预报模型对小区域强降水的TS评分为0.67,而逐步回归模型的TS评分仅为0.20.由此可见,人工神经网络具有较强的处理非线性问题能力,在小区域强降水应用中具有更好的预报效果.     

结合支持向量机和近邻法的太阳耀斑预报方法

为了提高太阳耀斑预报模型的预报精度,提出了一种结合支持向量机和近邻法(SVM-KNN方法)的太阳耀斑预报方法.将太阳耀斑预报问题看作一个模式识别问题,在此基础上建立新的预报方法.选择太阳活动区的特征参量作为预报因子,如果活动区未来48小时发生大于等于M级耀斑标识为正例样本,未发生耀斑为反例样本,由这些样本组成训练集代入SVM训练算法构造了耀斑预报模型.通过输入活动区的特征参量值,预报模型使用SVM-KNN分类算法预报该活动区未来2天内是否发生太阳耀斑.模拟预报结果表明,新方法比使用SVM方法具有较高的报准率,可以应用到其它太阳活动预报领域.     

一种LSTM-BP多模型组合水文预报方法

水文数据是具有时序性的非线性数据，具有高度的不确定性和复杂性。使用单一模型进行预报的结果常常不尽人意，因此本文基于LSTM和BP神经网络建立LSTM-BP多模型组合预报模型进行水文预报。以子午河流域洪水数据为例进行预报，实验结果表明，多模型组合预报模型的预报结果要优于单一模型，同时预报的稳定性和精确度也得到了提高，从而为水文预报提供了新的思路。     

实现准确预报的降水预报模型构建方法研究

研究降水预报模型的构建问题,提高预报准确度.在降水预报技术中,选用较多的预报因子构建预报模型并根据预报因子之间的联系训练得到准确模型,但受空气不稳定特性的影响使得预报因子之间的非线性联系极难准确描述,传统预报模型构建方法不能有效获取预报因子之间的联系,无法训练得到准确的预报模型,造成降水预报准确度不高的问题.为解决上述问题,提出模糊聚类算法构建降水预测模型的方法.根据空气的流动特性,采用模糊聚类算法分析预报因子内部的直接关联特性从而准确表述预报因子之间的联系,构建初始预报模型,并据最小二乘回归方法训练得到降水预报模型.实验表明,模糊聚类算法能够有效获取预报因子之间的联系,准确构建和训练预报模型,实现了降水的准确预报.     

铁路客运量的预报

本文讨论铁路客运量的预报问题。介绍了一种直观、简便、实用的 AR 模型的建模方法。用该法所建的东北某站铁路客运量模型及其多步预报结果,在实用中具有一定的精度和满意度。     

铁路客动量的预报

本文讨论铁路客运量的预报问题，介绍了一种直观，简便，实用的AR模型的建模方法，用该法所建的东北栽站铁路客运量模型及其多步预报结果，在实用中具有一定的精度和满意度。     

Transmembrane segments prediction and understanding using support vector machine and decision tree

In recent years, there have been many studies focusing on improving the accuracy of prediction of transmembrane segments, and many significant results have been achieved. In spite of these considerable results, the existing methods lack the ability to explain the process of how a learning result is reached and why a prediction decision is made. The explanation of a decision made is important for the acceptance of machine learning technology in bioinformatics applications such as protein structure prediction. While support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction, they are black box models and hard to understand. On the other hand, decision trees provide insightful interpretation, however, they have lower prediction accuracy. In this paper, we present an innovative approach to rule generation for understanding prediction of transmembrane segments by integrating the merits of both SVMs and decision trees. This approach combines SVMs with decision trees into a new algorithm called SVM_DT. The results of the experiments for prediction of transmembrane segments on 165 low-resolution test data set show that not only the comprehensibility of SVM_DT is much better than that of SVMs, but also that the test accuracy of these rules is high as well. Rules with confidence values over 90% have an average prediction accuracy of 93.4%. We also found that confidence and prediction accuracy values of the rules generated by SVM_DT are quite consistent. We believe that SVM_DT can be used not only for transmembrane segments prediction, but also for understanding the prediction. The prediction and its interpretation obtained can be used for guiding biological experiments.     

考虑社会网络关系的P2P借贷项目违约风险预测

针对P2P（Peer to Peer）借贷项目违约风险预测中财务信息不完全或质量较低、预测准确率不高等问题,提出了一种考虑平台社会网络关系的P2P借贷项目违约风险预测的方法。通过对P2P借贷平台社会网络相关信息进行分析,从社会资本的结构维度、关系维度和认知维度发掘其中具有风险预测价值的关键特征,即社会网络风险特征,并将这些特征作为预测指标用于违约风险预测,依据多种非线性预测方法分别构建基于传统财务指标预测模型和引入社会网络风险特征后的混合指标预测模型,并对模型的预测结果进行了对比分析。实验结果表明,P2P借贷社会网络关系中蕴含着与借贷项目违约风险显著相关的特征,通过对这些特征进行有效挖掘并将其合理引入P2P借贷项目违约风险预测模型,有助于提高借贷项目违约风险预测效果,为投资者的投资风险规避及P2P借贷市场风险管理提供支持。     

Self-tuning predictors with application to river flow prediction

The paper presents developments of recursive self-adaptive prediction algorithms, called ‘self-tuning predictors’, using some common estimation techniques, and their application to prediction of flow discharge of the river Tigris at Baghdad, Iraq. Four kinds of predictors, viz the least-square predictor, the minimum-variance predictor, a predictor using stochastic approximation, and a two-stage predictor, have been developed. Using available data for the River Tigris, prediction results have been obtained for the average daily discharge, the average monthly discharge and the average yearly discharge. In each type of prediction a number of models have been tried. The various prediction results are presented in graphical and in tabular forms for comparison.     

RBF神经网络在储层表征中的应用研究

研究利用RBF神经网络技术进行石油储层表征中有关储层参数的计算与岩性的识别;建立了储层参数(渗透率)预测模型与岩性识别模型,并利用该两个模型对未知样本进行预测,预测结果与实际测量结果相比具有较好的一致性,其渗透率预测精度与收敛速度较BP神经网络模型有了很大的提高;应用表明,RBF神经网络在储层表征问题中有着广阔的应用前景。     

基于连续时间量子游走的链路预测方法研究*

链路预测是复杂网络研究的基础问题之一。目前研究者们已经提出了许多链路预测的方法,其中大量的链路预测方法是基于经典随机游走。量子游走是经典随机游走的量子模拟。大量研究表明,在诸如图匹配、搜索等很多领域,基于量子游走的量子算法的性能远优于其对应的经典随机游走算法。但目前关于基于量子游走的链路预测算法几乎没有研究报道。本文提出了一种基于连续时间量子游走的链路预测方法。实验结果表明,连续时间量子游走链路预测结果的AUC值和经典随机游走的结果非常接近。而在Precision和Recall指标上,远优于经典随机游走的链路预测结果。     

Efficiently processing deterministic approximate aggregation query on massive data

Financial distress prediction is very important to financial institutions who must be able to make critical decisions regarding customer loans. Bankruptcy prediction and credit scoring are the two main aspects considered in financial distress prediction. To assist in this determination, thereby lowering the risk borne by the financial institution, it is necessary to develop effective prediction models for prediction of the likelihood of bankruptcy and estimation of credit risk. A number of financial distress prediction models have been constructed, which utilize various machine learning techniques, such as single classifiers and classifier ensembles, but improving the prediction accuracy is the major research issue. In addition, aside from improving the prediction accuracy, there have been very few studies that specifically consider lowering the Type I error. In practice, Type I errors need to receive careful consideration during model construction because they can affect the cost to the financial institution. In this study, we introduce a classifier ensemble approach designed to reduce the misclassification cost. The outputs produced by multiple classifiers are combined by utilizing the unanimous voting (UV) method to find the final prediction result. Experimental results obtained based on four relevant datasets show that our UV ensemble approach outperforms the baseline single classifiers and classifier ensembles. Specifically, the UV ensemble not only provides relatively good prediction accuracy and minimizes Type I/II errors, but also produces the smallest misclassification cost.     

Optimal formation of fuzzy rule-base for predicting process’s performance measures

For various physical processes, especially those demanding high cost or operational time, it becomes crucial to have accurate predictions of their key performance measures based on given settings of different input parameters. Among other artificial intelligence based tools, fuzzy rule-based systems have also been widely used for this purpose. Widespread applicability of the rule-based systems has been restricted by lack of accuracy in the prediction results and inherent difficulties in different approaches that have been utilized for improving their prediction capabilities. The paper presents a two-stage approach for enhancing accuracy of prediction results. The first stage seeks best possible assignment of fuzzy sets of a response variable to the rules of a fuzzy rule-base, while the second stage looks for further improvement by adjusting shapes of the fuzzy sets of the response variable. For accomplishment of both of the stages, simulated annealing algorithm has been utilized and the approach has been practically applied on experimental data related to a turning process. The process has resulted in development of a rule-base that predicts with highly acceptable levels of accuracy.     

基于支持向量机的混沌时间序列预测分析

混沌的特性决定了混沌系统很难长期预测,支持向量机有强大的学习能力,根据相空间重构理论用支持向量机建立预测模型对混沌时间序列进行短期预测。预测输出构建混沌吸引子来定性评价预测模型性能,同时与BP神经网络RBF神经网络构建的预测模型比较,计算预测模型的均方根误差定量地评价模型的性能。仿真结果表明,该方法具有较高的预测精度和泛化能力。     

Multi-step prediction error approach for controller performance monitoring

Performance monitoring of model predictive control (MPC) systems has received a great interest from both academia and industry. In recent years some novel approaches for multivariate control performance monitoring have been developed without the requirement of process models or interactor matrices. Among them the prediction error approach has been shown promising, but it is based on single-step prediction and may not be compatible with the MPC objective that is based on multi-step prediction. This paper develops a multi-step prediction error approach for performance monitoring of model predictive control systems, and demonstrates its application in a real industrial MPC performance monitoring and diagnosis problem.     

Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction

Neural networks have been employed in a multitude of transportation engineering applications because of their powerful capabilities to replicate patterns in field data. Predictions are always subject to uncertainty arising from two sources: model structure and training data. For each prediction point, the former can be quantified by a confidence interval, whereas total prediction uncertainty can be represented by constructing a prediction interval. While confidence intervals are well known in the transportation engineering context, very little attention has been paid to construction of prediction intervals for neural networks. The proposed methodology in this paper provides a foundation for constructing prediction intervals for neural networks and quantifying the extent that each source of uncertainty contributes to total prediction uncertainty. The application of the proposed methodology to predict bus travel time over four bus route sections in Melbourne, Australia, leads to quantitative decomposition of total prediction uncertainty into the component sources. Overall, the results demonstrate the capability of the proposed method to provide robust prediction intervals.