地表导电性能的光波遥感

本文描述了地表电导率光波遥感的两种方法，即间接和直接法。间接法是先从多光谱遥感数据用回归分析法得到地表特性，再利用各类地形特性所具有的电导率和介电常数的已有知识，推算出地表电导率和介电常数；直接方法是利用回归分析法，从多光谱遥感数据及实测试验场地的地表电导率和介电常数数据找到计算地表电导率和介电常数的最佳光谱段组合及系数，即回归系数，从而可从多光谱遥感数据直接计算出地表电导率和介电常数。两种方法的核心都是回归分析法。本文着重介绍了用SPOT卫星遥感图像资料及德国试验场地资料来进行的一种非线性回归分析方法的尝试。可以看到，这种非线性回归分析方法比通常的线性回归分析方法的结果有明显的改进。     

一种基于前向散射雷达的车辆目标自动识别方法

研究了一种基于前向散射雷达的车辆目标自动识别方法:分析了前向散射雷达回波与目标速度、轮廓等目标特性之间的关系;提出了主瓣对齐的功率谱预处理方法,采用主成分分析方法分析了不同类别车辆的前向散射雷达功率谱的特性;提出了基于多元线性回归的目标特征提取方法,比较了K最近邻法和Bayes分类器对不同特征集的识别性能.实现结果表明,本文采用的特征提取方法显著改善了识别效果.     

基于BP网络和遗传算法的正交实验分析

传统的实验设计与分析方法为首先进行正交实验设计,然后对实验结果进行回归分析和方差分析以确定最佳工艺条件。文章提出的基于BP网络和遗传算法的正交实验分析方法,利用BP网络的高度非线性拟合特性对复杂的多输入多输出问题进行较高精度的回归,运用遗传算法优越的全局并行随机搜索及对适应度函数广泛的适应性等特性进行最优工艺条件的搜索,克服了传统分析方法系统模型辨识困难、后续实验工作量大以及最佳工艺条件搜索困难等缺点,大大提高了实验工作的效率和质量。     

一种基于多重分形的软件衰退分析方法

为了提高软件衰退预测的精度,采用了多重分形分析方法,以系统资源参数时间序列为研究对象,提出了一种定性和定量相结合的分析方法,用以研究其波动规律.定性分析阶段,借鉴分形理论分析影响软件性能的系统资源参数,揭示参数的波动具有分形特性;且其多重分形谱特征能刻画系统运行过程中随时间变化的情况.定量预测阶段,提出了一种多维的Hlder指数计算方法,用于计算多个资源参数序列的Hlder指数,并采用自回归移动平均模型(ARMA)预测Hlder指数.最后进行了实证分析,结果表明,该方法具有较好的定性分析和定量预测能力.     

武器装备敏感性分析方法综述

武器装备敏感性分析是武器装备发展论证的重要内容,其方法的选用对结论的合理性,分析的可行性极为重要,为此需对已有方法进行对比分析.阐述了敏感性分析方法的分类,给出了筛选方法、局部敏感性方法和全局敏感性方法的适用范围;研究了全局敏感性分析方法中回归分析法、傅立叶振幅敏感性检验法、响应曲面法,互信息指数法和Sobol指数法等的思想、原理,并对其优缺点进行详细的对比分析;提出由于Sobol指数法对效能评估模型的线性、单调性以及输入的分布特性没有专门要求,并且能分析单个输入的主效应、全效应及多个输入的交互效应对模型输出的影响,以及分析成组输入因素对输出的影响,因而相对其它方法而言更加适用于武器装备敏感性分析.     

高压真空断路器机械特性在线监测技术研究现状与展望

介绍了高压真空断路器机械特性在线监测技术的基本内容,阐述了现有各种在线监测的具体方法,详细分析了各种振动信号分析方法及其适用范围,展望了高压真空断路器机械特性在线监测技术的发展趋势。     

一种结合感知与融合的视频质量评价新方法

提出了一种结合人眼视觉特性(HVS)和信息融合的视频质量评价新方法.该方法是在结构相似(SSIM)方法基础之上,融合了人眼几个主要视觉特性,如对比敏感度、多通道、视觉掩盖、视觉非线性等.新方法具有SSIM算法简单、高效等特性,同时又满足人眼视觉特性,更好地反映了人的主观感受.通过VQEG Phase I测试数据集的实验结果证明,该方法在非线性回归后相关系数、斯皮尔曼相关系数、线外率等指标均优于传统的其他视频质量评价算法,有效地提高了视频质量评价的主客观一致性.     

传感器特性的线性化处理与非线性误差

本文研究了传感器特性的各种线性化处理方法和对应的非线性误差。曲线逼近拟合方法包括理论拟合、过零旋转拟合、端点连线拟合和端点连线平移拟合;线性回归拟合则介绍了基于最小二乘原理的统计辨识拟合方法。以变间隙武电容传感器为例推导了各种拟合方法的直线方程和非线性误差的计算公式;设计制作了实验系统,给出了实验结果。结果表明,线性回归拟合和过零旋转拟合与端点连线平移拟合的非线性误差相同,但线性回归拟合方法能够处理包括其它确定因素和不确定因素在内的误差,能够给出传感器的精度指标。     

(R)-肾上腺素高产菌株发酵培养基的优化

为了充分发挥出突变株的高转化活性及其优良特性,采用响应面试验设计方法优化(R)-肾上腺素高产菌株Kocuria rhizophila(K.rhizophila).H5401的发酵培养基.用Plackett-Burman设计法研究初始发酵培养基中各成分对(R)-肾上腺素产率的影响,筛选出有显著效应的3个因素:甘油、蛋白胨和NH4H2PO4.利用最陡爬坡法逼近最大响应区域.通过Box-Behnken中心组合实验和响应面分析方法确定出3个主要影响因素的最佳浓度.实验结果经过回归分析拟合出1个2次方程,用其预报的最优培养基做摇瓶发酵试验,(R)-肾上腺素产率达61.07%,比优化前提高了24.89%,表明响应面试验设计优化方法有效提高了(R)-肾上腺素的产量,降低了生产成本,为后续放大实验提供依据.     

基于DTPN的时间Petri网的组件级化简规则

时间Petrl网(TPNs)是实时系统时间特性常用的描述和验证的Petri网模型.组件级化简方法是TPN模型常用的分析方法,在保持外部可观察时间特性的前提下,将组件TPN模型化简成一个很简单的TPN模型.然而它却失去了组件内部的性质,如冲突和并发等性质.文中引人延迟时间Petri网(DTPN),通过组件TPN模型向DTPN模型转化,使化简后模型既保持外部可观察时间特性,又保持组件内部的冲突和并发等性质.为了分析化简后的DTPN模型,文中还提出了一种新的DT-PN调度分析方法.最后通过对一个C2系统的组件TPN模型的分析实例,验证该方法的有效性.     

Robust Gaussian process modeling using EM algorithm

Gaussian process (GP) regression is a fully probabilistic method for performing non-linear regression. In a Bayesian framework, regression models can be made robust by using heavy-tailed distributions instead of using normal distribution for modeling noise. This work focuses on estimation of parameters for robust GP regression. In literature, these are learned by maximizing the approximate marginal likelihood of data. However, gradient-based optimization algorithms which are used for this purpose can be unstable or may require tuning. In this work, an EM algorithm based approach is derived and implemented to infer the parameters. The pros and cons of the two approaches are analyzed. The advantage of EM algorithm lies in its ease of implementation and theoretical guarantees of numerical stability and convergence while its prediction performance is still comparable to gradient-based approaches. In some cases EM algorithm may be slow to converge. To circumvent this issue a faster EM based approach known as Expectation Conjugate Gradient (ECG) is implemented on robust GP regression. Finally, the proposed EM approach to robust GP regression is validated using an industrial data set.     

Metamodeling by symbolic regression and Pareto simulated annealing

The subject of this paper is a new approach to symbolic regression. Other publications on symbolic regression use genetic programming. This paper describes an alternative method based on Pareto simulated annealing. Our method is based on linear regression for the estimation of constants. Interval arithmetic is applied to ensure the consistency of a model. To prevent overfitting, we merit a model not only on predictions in the data points, but also on the complexity of a model. For the complexity, we introduce a new measure. We compare our new method with the Kriging metamodel and against a symbolic regression metamodel based on genetic programming. We conclude that Pareto-simulated-annealing-based symbolic regression is very competitive compared to the other metamodel approaches.     

Fuzzy linear regression based on Polynomial Neural Networks

In this study, we introduce an estimation approach to determine the parameters of the fuzzy linear regression model. The analytical solution to estimate the values of the parameters has been studied. The issue of negative spreads of fuzzy linear regression makes the problem to be NP complete. To deal with this problem, an iterative refinement of the model parameters based on the gradient decent optimization has been introduced.In the proposed approach, we use a hierarchical structure which is composed of dynamically accumulated simple nodes based on Polynomial Neural Networks the structure of which is very flexible.In this study, we proposed a new methodology of fuzzy linear regression based on the design method of Polynomial Neural Networks. Polynomial Neural Networks divide the complicated analytical approach to estimate the parameters of fuzzy linear regression into several simple analytic approaches.The fuzzy linear regression is implemented by Polynomial Neural Networks with fuzzy numbers which are formed by exploiting clustering and Particle Swarm Optimization. It is shown that the design strategy produces a model exhibiting sound performance.     

Instance-Based Regression by Partitioning Feature Projections

A new instance-based learning method is presented for regression problems with high-dimensional data. As an instance-based approach, the conventional method, KNN, is very popular for classification. Although KNN performs well on classification tasks, it does not perform as well on regression problems. We have developed a new instance-based method, called Regression by Partitioning Feature Projections (RPFP) which is designed to meet the requirement for a lazy method that achieves high levels of accuracy on regression problems. RPFP gives better performance than well-known eager approaches found in machine learning and statistics such as MARS, rule-based regression, and regression tree induction systems. The most important property of RPFP is that it is a projection-based approach that can handle interactions. We show that it outperforms existing eager or lazy approaches on many domains when there are many missing values in the training data.     

Classification of EEG signals using neural network and logistic regression

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.     

Extended Gaussian Kriging for computer experiments in engineering design

Metamodeling or surrogate modeling is becoming increasingly popular for product design optimization in manufacture industries. In this paper, an extended Gaussian Kriging method is proposed to improve the prediction performance of widely used ordinary Kriging in engineering design. Unlike the forgoing approaches, the proposed method places a variance-varying Gaussian prior on the unknown regression coefficients in the mean model of Kriging and makes prediction at untried design points based on the principle of Bayesian maximum a posterior. The achieved regression mean model is adaptive, therefore capable of capturing more effectively the overall trend of computer responses and leading to a more accurate metamodel. Particularly, the regression coefficients in the mean model are estimated by a fast numerical algorithm, making extended Gaussian Kriging implemented roughly as efficient as ordinary Kriging. Experiment results on several examples are presented, showing remarkable improvement in prediction using extended Gaussian Kriging over ordinary Kriging and several other metamodeling methods.     

基于纹理约束和参数化运动模型的光流估计

提出了一种基于局部小平面运动的光流估计新方法。目的是获得精确致密的光流估计结果。与以往采用亮度一致性区域作为假设平面的算法不同，本算法利用序列图像的纹理信息，在纹理分割区域的基础上，进行运动估计。该算法首先通过微分法计算粗光流，可以得到参数化光流模型的初始估计，然后通过区域迭代算法，调整初始估计，从而得到精细的平面分割及其对应的参数化光流模型。基于纹理信息的部分拟合算法被用于算法的每一步当中，保证了纹理边缘位置的光流估计值的准确性。实验采用了标准图像序列，结果表明，可以得到更为精细的光流估计结果，特别是对于那些有着丰富纹理信息的室外环境的图像序列，而且在运动边界处的结果改善尤为明显。     

Nonlinear regression applied to interval-valued data

This paper introduces a nonlinear regression model to interval-valued data. The method extends the classical nonlinear regression model in order to manage interval-valued datasets. The parameter estimates of the nonlinear model considers some optimization algorithms aiming to identify which one presents the best accuracy and precision in the prediction task. A detailed prediction performance study comparing the proposed nonlinear method and other linear regression methods for interval variables is presented based on K-fold cross-validation scheme with synthetic interval-valued datasets generated on a Monte Carlo framework. Moreover, two suitable real interval-valued datasets are considered to illustrate the usefulness and the performance of the approaches presented in this paper. The results suggested that the use of the nonlinear method is suitable for real datasets, as well as in the Monte Carlo simulation study.     

fMRI脑图的感知状态分析 ——回归模型及其寻优的非同质检验

脑功能核磁共振图像fMRI的特点是定位准确,但信噪比低、数据量大。对fMRI数据的泛回归模型的超参数寻优问题作了分析,提出基于非同质检验的超参数确认方法,重点比较了它在线性和非线性的回归方式（包括岭回归,支持向量回归,Elman递归神经网络）下针对不同外界环境特征的回归能力差异,实验所采用原始数据均来自PBAIC2006,结果表明,该方法在对相关领域知识较少依赖的前提下,具有较好的稳定性和泛化能力;同时在所涉及到的回归方法当中,线性方法的实现简单、有效,在计算代价上低于其他方法,对多种外界特征具有较高的预测能力。     

基于多重线性回归模型的翻译等价对获取

翻译等价对在词典编纂、机器翻译和跨语言信息检索中有着广泛的应用。文章从双语句对的译文等价树中抽取翻译等价对。使用译文直译率、短语对齐概率和目标语-源语言短语长度差异等特征对自动获取的等价对进行评价。提出了一种基于多重线性回归模型的等价对评价方法,并结合N-Best策略对候选翻译等价对进行过滤。实验结果表明:在开放测试中,基于多重线性回归模型的等价对评价及过滤方法其性能要优于其它方法。