基于多层递阶回归分析的轧钢煤气用量预测

以某钢铁企业为背景,基于煤气用户的历史数据,通过多层递阶回归分析建立相应的消耗预报模型,从而对煤气用量进行预测.首先把统计样本中的各个高相关因子作为回归变量进行线性回归处理,然后以回归系数与预报因子的乘积作为对修正量来进行多层递阶预报.这种多层递阶与回归分析方法,既能较好地体现高相关因子在预报模型中的重要作用,又具有较强的适应性,可提高预报精度.实际工业应用证明了方法的有效性.     

多层递阶预报方法的改进及其在季节性时间序列预报中的应用

本文对多层递阶预报方法进行了深入的研究,提出了一种初值选取方法,并选择AR模型作为预报模型,采用高效的Houscholder变换来确定模型阶次,从而使得这种预报方法简便而实用,预报精度亦有进一步提高。本文还提出一种季节性时间序列预报的新方法,它具有简单、易行,且精度高的特点。本文将多层递阶预报方法成功地应用于冷库自动控制系统,实现了对温度的实时预报,取得了满意的效果。     

一类模糊系统的多层递阶预报

本文讨论一类模型系统的多层递阶预报问题，提出了相应的模糊多层递阶模型，模糊参数估计方法及预报步骤。通过应用实例说明效果良好。     

风浪中船舶横摇仿真与预报研究

对船舶横摇运动进行仿真与预报研究可大大提高船舶航行安全性,更好地保障船舶各项作业的顺利进行.从频谱分析的角度对海上随机风浪进行仿真,并根据刚体平衡原理建立船舶运动方程,由此仿真船舶的横摇运动.在此基础上,针对舰船运动姿态非线性、非平稳的时域特点,运用改进AR模型的多层递阶方法和自回归方法对船舶横摇运动进行预报,结合预报误差指标进行两种预报方法的比较.预报结果表明,多层递阶方法在一定程度上提高了船舶运动姿态的预报精度和预报时间长度,可较好实现非线性预报.     

多层递阶辨识方法

本文提出了作为多层递阶预报方法的基础的多层建模的方法,称为多层递阶辨识方法,并 指出这种方法可能是解决非线性系统辨识问题的一种有效的途径.     

多层递阶方法理论与应用的进展

多层递阶方法把非线性模型化成与之输入输出等价的多层线性模型,并强调其模型参数的时变性,所以在解决预报问题方面取得了较好的效果。对这一方法的理论与应用研究的进展情况进行介绍。     

多模型多方法综合多层递阶预报模式在油田产量预报中的应用

本文介绍了多模型多方法综合多层递阶预报模式在油田产量预报中的应用情况,指出了这种模式在油田中应用的合理性。给出了适用于某油田的综合模式的模型族和算法族,报告了实际应用的结果。     

多层递阶预报方法的改进及其应用

本文对韩志刚提出的“多层递阶预报方法”作了改进,其中涉及松驰因子的选取,初值确定,非平稳序列的处理,参数建模等方面,还给出了预报的置信区间。改进后的方法经过验证和在上海地区用电量分季度预报中的应用证明优于原方法。     

天气系统的建模与预报

本文讨论了天气系统预报模型的建模和预报问题,说明了由于有了多层递阶预报方法,所以使得具有时变参数的线性预报模型在预报问题中具有一定的普遍性,并给出了应用效果的报告。     

黑龙江省五月降水环流形势的多层递阶长期预报模型

本文应用多层递阶预报方法,对黑龙江省五月降水环流形势进行了长期预报,使预报的趋势准确率和精度得到了较大的提高。     

Ensembles for multi-target regression with random output selections

We address the task of multi-target regression, where we generate global models that simultaneously predict multiple continuous variables. We use ensembles of generalized decision trees, called predictive clustering trees (PCTs), in particular bagging and random forests (RF) of PCTs and extremely randomized PCTs (extra PCTs). We add another dimension of randomization to these ensemble methods by learning individual base models that consider random subsets of target variables, while leaving the input space randomizations (in RF PCTs and extra PCTs) intact. Moreover, we propose a new ensemble prediction aggregation function, where the final ensemble prediction for a given target is influenced only by those base models that considered it during learning. An extensive experimental evaluation on a range of benchmark datasets has been conducted, where the extended ensemble methods were compared to the original ensemble methods, individual multi-target regression trees, and ensembles of single-target regression trees in terms of predictive performance, running times and model sizes. The results show that the proposed ensemble extension can yield better predictive performance, reduce learning time or both, without a considerable change in model size. The newly proposed aggregation function gives best results when used with extremely randomized PCTs. We also include a comparison with three competing methods, namely random linear target combinations and two variants of random projections.     

An inferential modeling method using enumerative PLS based nonnegative garrote regression

In this paper a robust linear regression method with variable selection is proposed for predicting desirable end-of-line quality variables in complex industrial processes. The development of such prediction models is challenging because there is usually a large pool of candidate explanatory variables, limited sample data, and multicollinearity among explanatory variables. The proposed method is named as the enumerative partial least square based nonnegative garrote regression. It employs partial least square regression in enumerative manner to generate initial model coefficients and then uses a nonnegative garrote method to shrink original coefficients so that irrelevant variables can be eliminated implicitly. Analysis about the advantages of the proposed method is provided compared to existing state-of-art model construction methods. Two simulation examples as well as an industrial application in a local semiconductor factory unit are used to validate the proposed method. These examples witness substantial improvement in terms of accuracy and robustness in variable selection compared to existing methods. Specifically, for the industrial case the percentages of improvement in terms of root mean squared error is up to 24.3% compared with the previous work.     

Neural network based iterative learning predictive control design for mechatronic systems with isolated nonlinearity

The paper presents a new nonlinear predictive control design for a kind of nonlinear mechatronic drive systems, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. The nonlinear system is first treated into an equal linear time-variant system plus a nonlinear part using a neural network, then an iterative learning linear predictive controller is developed with a similar structure of PI optimal regulator and with setpoint feed forward control. Because the overall control law is a linear one, this design gives a direct and also effective multi-step prediction method and avoids the complicated nonlinear optimization. The control law is also an accurate one compared with traditional linearized method. Besides, changes of the system state variables are considered in the objective function with control performance superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is compared with conventional state space predictive control method and classical PI optimal control method. Tracking performance, robustness and disturbance rejection are enlightened.     

引入时间效应的SVD++线性回归推荐算法

针对传统协同过滤算法中的数据稀疏问题,在SVD++算法和线性回归模型的基础上引入时间效应属性,提出一种推荐算法timeSVD++LR。采用SVD++算法将用户和项目信息与隐式反馈信息相融合映射到隐语义空间,将用户和项目之间的交互作用建模为该空间中的内积。通过描述用户和物品在各因子上的特征来解释评分值,在此基础上对时间效应建模,进一步提高预测结果的准确度。根据预测评分矩阵构造特征向量,将原始训练数据作为线性回归模型的输入,采用梯度下降算法优化最终代价函数,生成使得代价函数值最小的参数向量,同时将特征向量和参数向量代入预测模型求解预测评分。在MovieLens数据集上的实验结果表明,与RSVD、SVD++和timeSVD++算法相比,该算法的平均绝对误差和均方根误差均较低,其推荐准确性较高。     

Unbiased regression trees for longitudinal and clustered data

A new version of the RE–EM regression tree method for longitudinal and clustered data is presented. The RE–EM tree is a methodology that combines the structure of mixed effects models for longitudinal and clustered data with the flexibility of tree-based estimation methods. The RE–EM tree is less sensitive to parametric assumptions and provides improved predictive power compared to linear models with random effects and regression trees without random effects. The previously-suggested methodology used the CART tree algorithm for tree building, and therefore that RE–EM regression tree method inherits the tendency of CART to split on variables with more possible split points at the expense of those with fewer split points. A revised version of the RE–EM regression tree corrects for this bias by using the conditional inference tree as the underlying tree algorithm instead of CART. Simulation studies show that the new version is indeed unbiased, and has several improvements over the original RE–EM regression tree in terms of prediction accuracy and the ability to recover the correct tree structure.     

基于LLE与BA-Elman的瓦斯涌出量动态预测研究

针对瓦斯涌出量受诸多因素影响,彼此间存在复杂的非线性关系导致预测精度不高这一问题,提出基于相关分析理论和局部线性嵌入理论的Elman网络瓦斯涌出量动态预测方法。在对监测指标进行相关性分析的基础上,用局部线性嵌入理论实现瓦斯涌出量影响因素从高维空间至低维空间的映射,进而重构影响瓦斯涌出量的有效因子,并将其作为Elman网络预测模型的输入矢量,以降低模型结构的复杂度,同时用蝙蝠算法全局优化Elman模型以提高预测的精度和泛化能力。试验结果表明该动态预测模型泛化能力强,预测精度高,适用于实际工作中对瓦斯涌出量的预测。     

基于小波的搜索量聚类及在变量选择中的应用

针对使用网络购物搜索量数据建立预测模型时的变量选择问题,提出一种基于连续小波变换(CWT)及其逆变换的聚类方法。算法充分考虑了搜索量的数据特征,将原始序列分解成为不同时间尺度下的周期成分,并重构为输入向量。在此基础上通过加权模糊C均值(FCM)方法进行聚类。变量选择是根据聚类后每个分类中的关键词隶属度函数值确定的,选择效果通过我国居民消费价格指数(CPI)的预测模型进行验证。结果表明,搜索量序列具有不同长度的周期成分,聚类后同组关键词具有明显的商品类型一致性。与其他变量选择方法相比,基于小波重构序列聚类的预测模型具有更高的预测精度,单步和三步预测相对误差仅为0.3891%和0.5437%,预测变量也具有清晰的经济含义,因此特别适用于解决大数据背景下高维预测模型的变量选择问题。     

Regression analysis of belief functions on interval-valued variables: comparative studies

In real engineering, the observations of process variables are usually imprecise, uncertain, or both. In such cases, the general process modeling approaches cannot be implemented. In this paper, we investigate on the parametric and nonparametric evidential regression of imprecise and uncertain data, represented as belief function on interval-valued variables. The parametric evidential regression includes both multiple linear and nonlinear evidential regression models. The nonlinear evidential regression model is derived by introducing kernel function into the multiple linear evidential regression model. The parametric evidential regression models are identified by using evidential EM algorithm, an evidential extension of the EM algorithm. In the nonparametric evidential regression, the prediction for a given input vector is computed using a nonparametric, instance-based approach: the training samples in the neighborhood of the given input vector provide pieces of evidence reflecting the values taken by such input vector, these pieces of evidence are combined to form the prediction. Some unreliable sensor experiments are designed to validate the performances of the proposed parametric and nonparametric evidential regression models. With comparative studies, we get some interesting results.     

基于灰色线性回归模型的预测控制

针对大滞后线性时不变控制系统,将Smith预估控制方法和灰色线性回归预测方法相结合,提出一种改进型预测控制方法.该方法设计一种灰色线性回归预测器,将其置于反馈回路,预测下一时刻的输出值,达到超前控制的效果.同时根据预测精度来选择控制方案,达到精度要求时,在Smith预估控制中加入灰色线性回归预测控制器,否则就只采用Smith预估控制.仿真结果表明该方法有效地克服了大滞后对控制系统性能的影响,提高了控制精度.     

A methodology for brand feature establishment based on the decomposition and reconstruction of a feature curve

For creative products, maintaining original brand elements and features in a new product is an important issue in the design process as brand features are conceived and generated for longevity. However, current methods rely on designers’ abilities, and the size of forms is easily affected when shape morphing is applied, causing limitations in computer-aided design. In order to focus on design while preserving key features, a systematic method for presenting brand features is proposed in this article. In this method, the feature curves of the brand features of a company are decomposed with defined feature parameters, which were then used to reconstruct the feature curve of the designed product in the design stage by using a residual modified gray prediction model. A classic vehicle configuration design is taken as an example to show the implementation procedure of the proposed method. With residual modification, this method can also assimilate other forms from the original form database, and generate new forms based on gray prediction. The results show that brand features can be retained in the newly designed product based on the proposed method. Though vehicle design is taken as the example, this method can also be used to develop designs for many other the brand features. For classic products with historical value, this method can generate new forms that maintain original brand features, thereby satisfying customers’ needs for brand authenticity.