序贯实验设计方法在变换反应动力学方程模型参数估值研究中的应用

根据序贯实验设计原理,提出用于 B_(110-2)催化剂的动力学模型参数估值程序,并提出B_(110-2)催化剂的中温变换反应本征动力学方程。     

基于数据拟合的摄像机标定和图像的数值校正

采用的摄像机成像模型考虑了透镜的径向畸变和切向畸变。标定过程包括对摄像机的内外部参数的直接线性变换估算和基于内部映射牛顿法的子空间置信域法的非线性优化算法优化。同时提出了对畸变图像校正的数值计算方法。     

序贯参数估算

一、前言参数估算一般可分为批量参数估算(Batchwise Parameter Estimation)与序贯参数估算(Sequential Parameter Estimation)。通常我们所指的参数估算是批量参数估算。即通过实验数据,根据已知的数学模型,建立目标函数,调用最优化计算方法,使目标函数达到极小时的参数,就是我们所求的参数估算值。这种方法的特点在于,对于每一个已知的实测值,都是等权处理。换句话说,所有的实测值其重要程度都是相同的。特点二在于,对于全区间上的已知点,计算目标函数的次数少则数次、多则数百次。即计算量相     

面向烟草企业的数据挖掘中分类算法分析与量化研究

采用交叉验证方法针对C4.5、Bayesian置信网络、序贯最小优化(SMO)三种主流数据挖掘分类算法对中国烟草年鉴2011-2012中的销售数据进行了实验分析,分别得出了在相同训练、测试样本数据下三种算法建立模型所需时间、分类准确性、覆盖率及Margin曲线。分析了训练样本数量对三种算法的不同影响,为使用者在不同的样本质量下选择相应的分类算法提供理论和实验依据。     

专家干预下置信规则库参数训练的差分进化算法

传统关于置信规则库参数训练模型的求解主要采用FMINCON函数及群智能算法,但在算法设计中并未涉及所有的置信规则库参数,且缺少必要的专家干预.为解决这些问题,首先在现有参数模型的基础上进一步扩宽参与参数训练的置信规则库参数,然后设计出符合思维逻辑的专家干预的约束条件,最后结合差分进化算法提出具有更高收敛精度的置信规则库参数训练方法.在实验分析中,首先在多极值函数的实例中分析该方法的有效性,再在输油管道检漏的实例中检验专家干预的合理性及对比现有的其他参数训练方法.实验结果表明,该方法是有效可行的.     

非均匀表面两阶段吸附模型的改进

非均匀表面两阶段吸附模型是适用于各种类型吸附等温线的表面活性剂固液界面吸附热力学模型，为了更好地反映第3组分（助剂、第二表面活性剂、无机盐和碱等），对表面活性剂吸附的影响。本文对非均匀表面两阶段吸附模型进行改进，导出了新的能用于有极限吸附和无极限吸附体系的吸附等温方程式。用此方法式关联含第3级分体系的吸附实验结果，计算值和实验值符合很好。     

基于多模型假设检验的认知无线电频谱感知方法

提出一种基于序贯概率似然比多模型假设检验的认知无线电协作频谱感知方法,用于检测可能含有不同结构和参数不确定性的未知信号.传统的认知无线电协作频谱感知方法(如基于序贯概率似然比的单模型假设检验、M元假设检验等),仅限于处理已知信号分布,不考虑信号分布的不确定性,可能会造成检测误判.所提出方法不仅可以处理认知无线电信号分布模型的不确定性问题,而且可以得到满足错误概率约束的有效检测.对频谱感知的一个典型场景进行仿真实验,结果表明所提出基于序贯概率似然比多模型假设检验方法相对于传统方法的检测有效性.     

语音识别确认中的置信特征和判定算法

提出了一种基于支持向量机的联合多种置信特征进行语音识别确认的判定方法.从待确认语音中提取出分段的后验概率和线性预测编码识别结果置信特征,其中后验概率根据垃圾模型近似计算得到;设计支持向量机分类器联合多种置信特征给出最终确认结果.实验结果表明,所提出的置信特征和支持向量机分类器取得了很好的确认效果.     

不完全信息议价博弈的序贯均衡分析与计算实验

本文从理论研究和计算实验两个层次分析和验证了一类带有时间 偏好的单边双类型不完全信息议价博弈模型及其序贯均衡, 运用单阶段偏离法则分别推导和证明了该议价博弈的合并均衡与分离均衡, 并通过策略比较和构造静态出价博弈证明了合并均衡是议价博弈的唯一理性解. 在此基础上, 本文设计不完全信息议价博弈计算实验场景, 基于协同演化计算实验方法验证了议价博弈的序贯均衡解. 最后, 本文探讨了该序贯均衡对于议价双方相应管理策略的实践指导意义.     

基于Local约简的序贯三支分类器

序贯三支决策是三支决策理论近年发展起来的一种新型决策方法。传统的序贯三支决策方法鲜有针对序贯信息粒的构建和其在分类学习中的应用的研究。针对这两个问题,研究了Local约简与Global约简之间的内在序贯性,并以此构建了具有约简特性的序贯信息粒。在此基础上设计了一种序贯三支分类器。实验结果表明,该序贯三支分类器不仅能很好地在合适信息粒上进行分类,而且较传统的分类算法提高了数据集的分类精度。     

On sequential confidence estimation of parameters of stochastic dynamical systems with conditionally Gaussian noises

We consider the problem of non-asymptotical confidence estimation of linear parameters in multidimensional dynamical systems defined by general regression models with discrete time and conditionally Gaussian noises under the assumption that the number of unknown parameters does not exceed the dimension of the observed process. We develop a non-asymptotical sequential procedure for constructing a confidence region for the vector of unknown parameters with a given diameter and given confidence coefficient that uses a special rule for stopping the observations. A key role in the procedure is played by a novel property established for sequential least squares point estimates earlier proposed by the authors. With a numerical modeling example of a two-dimensional first order autoregression process with random parameters, we illustrate the possibilities for applying confidence estimates to construct adaptive predictions.     

An inquiry into the applicability of Kantorovich’s idea to the thermodynamic optimization

A feasible region is employed to describe unknown parameters in thermodynamic models. The method is based on interval estimation of experimental observations, and it is applied to thermodynamic optimization of liquid phase in Ag–Mg system. A final feasible region is an overlapping area among all feasible regions calculated from different groups of data. A discussion of computational advantages on this method suggests that it is a promising method for parameter estimation, but its limitations require more studies on problems such as non-linear inequalities, multi-dimensional visualization and uncertainties in experimental conditions.     

Maximum likelihood estimation of markov-process blob boundaries in noisy images

Effective and elegant procedures have recently appeared in the published literature for determining by computer a highly variable blob boundary in a noisy image [1]-[3]. In this paper we point out that if the blob boundary is modeled as a Markov process and the additive noise is modeled as a white Gaussian noise field, then maximization of the joint likelihood of the hypothesized blob boundary and all of the image data results in roughly the same blob boundary determination as does one of the aforementioned deterministic formulations [2]. However, the formulation in this paper provides insights into and optimal parameter values for the functions involved and reveals suboptimalities in some of the formulations appearing in the literature. More generally, we agree that maximization of the joint likelihood of the hypothesized blob boundary and of the entire picture function is a fundamental approach to boundary estimation or the estimation of linear features (roads, rivers, etc.) in images, and provides a powerful mechanism for designing sequential, parallel, or other boundary estimation algorithms. The ripple filter, an advanced form of region growing, is briefly introduced and illustrates one of a number of alternative algorithms for maximizing the likelihood function. Hence, this likelihood maximization approach provides a unified view for seemingly different approaches, such as sequential boundary finding and region growing. Bounds on the accuracy of boundary estimation are readily derived with this formulation and are presented.     

An efficient sensor quantization algorithm for decentralized estimation fusion

In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems. First, we derive a fixed-point-type necessary condition for both optimal sensor quantization rules and an optimal linear estimation fusion rule: a fixed point of an integral operation. Then, we can motivate an iterative Gauss–Seidel algorithm to simultaneously search for both optimal sensor quantization rules and an optimal linear estimation fusion rule without Gaussian assumptions on the joint probability density function (pdf) of the estimated parameter and observations. Moreover, we prove that the algorithm converges to a person-by-person optimal solution in the discretized scheme after a finite number of iterations. It is worth noting that the new method can be applied to vector quantization without any modification. Finally, several numerical examples demonstrate the efficiency of our method, and provide some reasonable and meaningful observations how the estimation performance is influenced by the observation noise power and numbers of sensors or quantization levels.     

Interval estimation for a Pareto distribution based on a doubly type II censored sample

For a complete sample, Chen [Chen, Z., 1996. Joint confidence region for the parameters of a Pareto distribution. Metrika 44, 191-197] proposed an interval estimation of the parameter θ and a joint confidence region of two parameters of a Pareto distribution. When the first r lifetimes and the last s lifetimes out of n inspected items are missing, doubly type II censoring has arisen. Since Chen’s method cannot be extended to the doubly type II censored sample case, I proposed another joint confidence region for the two parameters of a Pareto distribution. The interval estimation of parameter ν is also given for a doubly type II censored sample. Since the complete sample case (r=0) and the right type II censored sample case (r=s=0) are special cases of doubly type II censored samples, the proposed confidence region should also be appropriate for these two special cases, and thus can be compared with Chen’s method based on the area of the confidence region. From the simulation results, it can be found that the proposed method is better than Chen’s method in obtaining a smaller confidence area. But the difference in area of the two methods becomes very slight when the sample size becomes larger. In this paper, I also proposed the prediction intervals of the future observation and the ratio of the two future consecutive failure times based on the doubly type II censored sample. Finally, an example is given to illustrate the proposed method.     

Designing priors for robust Bayesian optimal experimental design

Building mathematical models is a common task in process systems engineering, which requires estimation of model parameters as the final step of modeling exercise. Model based experimental design has evolved as a potential statistical tool for reducing uncertainties in parameter estimates. Often a huge volume of process information is generated as an end result of an experimental design. Designing optimal experiments based on current or prior process knowledge is still an open research problem. This paper deals with how information, available a priori, can be organized and systematically used for designing robust Bayesian dynamic experiments, in the presence of process constraints. The designed experiments are ‘robust’ to a poor choice of nominal parameter values. Several novel techniques for organizing a priori process knowledge are explored from a theoretical view point. The influence of proposed prior designs on parameter estimates is demonstrated on a semi-continuous baker's yeast fermenter problem.     

Fault tolerant approach for biosignal-based robot control

This paper proposes a fault tolerant framework for biosignal-based robot control with multiple sensor electrodes. In this approach, to cope with sensor faults, a reliable joint torque estimation model is selected from a group of estimation models based on sensor failure classifiers. The correlation among the electromyography (EMG) signal streams is used as input feature vectors for fault detection. To validate our proposed method, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow-joint torque estimation experiments with five participants. When one EMG sensor electrode experiences one of the problems, the experimental results show that the joint torque can be estimated with significantly fewer errors using our proposed approach than a joint torque estimation method without sensor fault detection or than a method with a conventional sensor fault detection algorithm. Furthermore, we controlled a mannequin-arm-attached one-DOF exoskeleton based on the estimated torque profiles by generating movements with the estimated torque derived from the selected model.     

SnO_2-LiZnVO_4系纳米湿敏陶瓷湿敏特性研究

采用共沉淀法制备出SnO2-L iZnVO4系纳米湿敏粉体,考察了液相掺杂L iZnVO4对材料湿敏特性的影响,并从氧化物表面的吸附理论出发,给出了反映湿敏特性的微观参数n值,湿敏线性越好的材料其微观参数n值与0.5越靠近。实验结果表明:适当的L iZnVO4液相掺杂可使材料具有低湿电阻小、灵敏度适中的特性。理论计算得到的阻湿特性参数与实验结果符合得很好。     

Reliability-based design optimization with confidence level under input model uncertainty due to limited test data

For obtaining a correct reliability-based optimum design, the input statistical model, which includes marginal and joint distributions of input random variables, needs to be accurately estimated. However, in most engineering applications, only limited data on input variables are available due to expensive testing costs. The input statistical model estimated from the insufficient data will be inaccurate, which leads to an unreliable optimum design. In this paper, reliability-based design optimization (RBDO) with the confidence level for input normal random variables is proposed to offset the inaccurate estimation of the input statistical model by using adjusted standard deviation and correlation coefficient that include the effect of inaccurate estimation of mean, standard deviation, and correlation coefficient.     

Getting the most out of it: Optimal experiments for parameter estimation of microalgae growth models

Mathematical models are expected to play a pivotal role for driving microalgal production towards a profitable process of renewable energy generation. To render models of microalgae growth useful tools for prediction and process optimization, reliable parameters need to be provided. This reliability implies a careful design of experiments that can be exploited for parameter estimation. In this paper, we provide guidelines for the design of experiments with high informative content based on optimal experiment techniques to attain an accurate parameter estimation. We study a real experimental device devoted to evaluate the effect of temperature and light on microalgae growth. On the basis of a mathematical model of the experimental system, the optimal experiment design problem was formulated and solved with both static (constant light and temperature) and dynamic (time varying light and temperature) approaches. Simulation results indicated that the optimal experiment design allows for a more accurate parameter estimation than that provided by the existing experimental protocol. For its efficacy in terms of the maximum likelihood properties and its practical aspects of implementation, the dynamic approach is recommended over the static approach.