Regressor and structure selection in NARX models using a structured ANOVA approach

Regressor selection can be viewed as the first step in the system identification process. The benefits of finding good regressors before estimating complex models are especially clear for nonlinear systems, where the class of possible models is huge. In this article, a structured way of using the tool analysis of variance (ANOVA) is presented and used for NARX model (nonlinear autoregressive model with exogenous input) identification with many candidate regressors.     

Automatic eye fixations identification based on analysis of variance and covariance

Eye movement is the simplest and repetitive movement that enables humans to interact with the environment. The common daily activities, such as reading a book or watching television, involve this natural activity, which consists of rapidly shifting our gaze from one region to another. In clinical application, the identification of the main components of eye movement during visual exploration, such as fixations and saccades, is the objective of the analysis of eye movements: however, in patients affected by motor control disorder the identification of fixation is not banal. This work presents a new fixation identification algorithm based on the analysis of variance and covariance: the main idea was to use bivariate statistical analysis to compare variance over x and y to identify fixation. We describe the new algorithm, and we compare it with the common fixations algorithm based on dispersion. To demonstrate the performance of our approach, we tested the algorithm in a group of healthy subjects and patients affected by motor control disorder.     

Automating the analysis of variance of orthogonal designs

A new algorithm is presented which for the wide class of orthogonal designs is capable of deducing the appropriate analysis of variance from the design only. As a consequence the use of a model equation for specifying the analysis becomes dispensable. The proposed approach can simplify the analysis of complex models with iterative crossing and nesting of factors, where treatment factors have fixed and plot factors have random effects. An implementation is described and its use is illustrated with several examples.     

Uanova: a fortran IV program for unbalanced nested analysis of variance

A FORTRAN IV program (UANOVA) to carry out unbalanced analyses of variance, associated variance component testing using synthesized error mean squares and degrees of freedom where appropriate, and the generation of summary statistics is presented. Data transforms are available to assist in meeting the requirements of the ANOVA method, and the data structure is defined as part of the input stream. The summary statistics include estimates of the population mean and variance, and confidence limits for the mean and variance components. The latter are approximations for levels higher than the first, however their calculation affords on indication of the imprecision of the point estimates. The program has been verified, where possible, with published data sets, results of these tests are presented.     

Estimating variance components with BMDP for analyses of technical reliability

Measurement reliability reflects the ratio of true (subject) to observed (subject plus error) variance. Analyses of reliability are limited due to difficulties in obtaining accurate estimates of the sources of variation upon which this relationship depends. This paper presents a simple analysis of variance procedure, using BMDP8V, for estimating the necessary variance components of mixed models, from which reliability may be assessed.     

Design of practical optimum JIT systems by integration of computer simulation and analysis of variance

This study introduces a framework for re-design of manufacturing systems into practical optimum just-in-time systems by integration of computer simulation and analysis of variance. The conventional JIT approach is mostly applicable to static production systems and the dynamic production systems require a more practical integrated JIT approach. In addition, the re-design of existing dynamic systems into just-in-time systems must follow a practical path, which can be a cumbersome task. This means, a unique practical optimum just-in-time system that considers system's limitations and its dynamic behavior must be designed. To achieve the objective of this study, first, the actual system must be totally modeled and simulated. Second, the integrated simulation model is tested and validated by analysis of variance. Third, the optimum (most fitted) JIT design is developed and tested by modeling actual system's limitations and its dynamic behavior. The framework is applied and tested for an auto production line and a heavy rolling mill workshop.     

方差分析数据处理系统的设计

利用Visual Basic设计了方差分析数据处理系统。简单介绍了方差分析的概念及作用,详细介绍了方差分析数据处理系统的设计,并对所设计的数据处理系统进行了性能评价。用方差分析数据处理系统考察影响油页岩渣浸出过程,发现B(碱液浓度)(F=73.1616)和C(油页岩渣粒径)(F=82.0762)是影响浸出过程的主要影响因素。表明本系统能在化学工业生产领域发挥良好的作用。     

Analysis of variance for fuzzy data

The method for imposing imprecise (fuzzy) data upon the traditional ANOVA model is proposed in this article. We transact the h-level sets of fuzzy data for the sake of invoking traditional method of ANOVA for real-valued data. We propose the decision rules that are used to accept or reject the null and alternative hypotheses with the notions of pessimistic degree and optimistic degree by solving optimization problems. Finally, we provide a computational procedure and an example to clarify the discussions in this article.     

基于双因素方差分析的推荐算法

提出一种新的基于双因素方差分析的推荐算法DFAR.该方法基于成熟的统计学模型,简单易理解,具有很好的鲁棒性.实验结果证明,该算法相比传统的项目协作过滤算法取得了更好的推荐效果,并大大节省了算法所需要的空间.     

基于变异系数的模糊传感器数据融合方法

在多传感器信息融合过程中,有时要利用多传感器对某一对象进行不同方面观测,而数据融合的目的是要对研究对象进行类型识别。根据统计分析理论,利用变异系数,给出了一种新的模糊传感器数据的融合算法。该方法不需要假设总体的分布类型,不需要定义受主观因素影响的关系矩阵,可以充分利用测量数据,避免丢失重要的极端数据,使结论更符合实际。仿真实验表明:算法有效,非常简单。     

Analysis of variance of Gabor filter banks parameters for optimal face recognition

Gabor filter banks constitute a very robust tool to extract discriminant information from a visual scene. After the now “classical” bank with 5 frequencies and 8 orientations proposed by Lades et al. and Wiskott et al., many other parametrizations of a Gabor filter bank have appeared. In order to find the optimal parametrization for a face recognition experiment, we have performed a 6-way analysis of variance of Gabor parameters using FERET, FRAV2D, FRAV3D, FRGC and XM2VTS face databases, including frontal and turned poses, facial expressions, occlusions and changes of illumination. Considering independent criteria to find the optimal Gabor filter bank, the bank with the highest recognition rate was found to have 6 frequencies and narrower Gaussian widths in the space domain. These results were obtained with Mahalanobis distance for a k-NN classifier, with analytical and holistic Gabor feature vectors. Moreover about 20% of the banks studied here obtained in average a better performance than the classical bank. For most of the databases considered, the highest recognition rates have been achieved with analytical representations (frontal images, images with turns or occlusions), with a holistic preponderance for images with gestures or changes of illumination. The inferiority found for holistic Gabor representations versus their analytical counterparts can be explained for the intrinsic redundancy and the size of the feature vectors of this kind of representation.     

Variance analysis of identified linear MISO models having spatially correlated inputs,with application to parallel Hammerstein models

This contribution concerns variance analysis of linear multi-input single-output models when the inputs are temporally white but where different inputs may be correlated. An expression is provided for the variance of a linearly parametrized estimate of the frequency response function from one block, i.e. from one input to the output. In particular, this expression reveals that the variance increases in one block when the number of estimated parameters in another block is increased, but levels off when the number of parameters in the other block reaches the number of parameters in the block in question. It also quantifies exactly how correlation between inputs affects the resulting accuracy and a graphical representation is provided for this purpose. The results are applicable to parallel MISO Hammerstein models when the nonlinearities are known and generalize an existing variance expression for this type of model.     

State-space analysis and identification for a class of hysteretic systems

In this paper we present results on the twin subjects of system analysis and system identification for a class of state-space realizable dynamic systems under the influence of hysteresis. The class of systems in question consists of models in the form of a linear time-invariant dynamic system in series with a differential model of hysteresis. It will be demonstrated that under fairly light constraints on the differential model of hysteresis, it is possible to design a series of experiments leading towards the identification of the full state-space realization. The approach is tested successfully on a high-precision mechanical translation system affected by hysteresis.     

Quantifying the accuracy of Hammerstein model estimation

This paper investigates the accuracy of the linear component that forms part of an overall Hammerstein model-structure estimate, and a key finding is that the process of estimating the non-linear element can have a strong effect on the associated estimate of the linear dynamics. Furthermore, this effect is not explained simply by way of considering how the input spectrum is changed by the non-linearity. Instead, it arises that the linear model-estimate variability may be dominated by a term that depends on the frequency response of the linear system itself. Amongst other things, the main results derived here have experiment design implications for Hammerstein system estimation.     

New insights on the analysis of nonlinear time-delay systems: Application to the triangular equivalence

In this paper we present recent advances towards an algebraic framework for the analysis and control of nonlinear systems with delays. We show the usefulness of these results by applying them to obtain a constructive characterization of the equivalence of a given system to the so-called triangular form.     

热带气旋客观定位的红外亮温方差方法

目的 热带气旋（TC）是生成于热带或副热带洋面上的强烈天气系统。在TC的监测分析和预报工作中,准确地确定其中心实时地理位置至关重要。此外,TC的精确位置也是TC强度估计的重要参数。对此,提出一种利用偏差角方差定位TC中心的方法。方法 首先,从红外卫星云图中截取热带气旋主体云系区域,并分别利用Bezier直方图和K均值聚类方法分割得到主体云系二值图像和红外亮温变化剧烈位置二值图像。其中,主体云系二值图像可将TC的主体云系从卫星红外云图中分割提取出来,用割提取出来的图像进行定位可以剔除掉外散环流的小云块对定位结果的影响;而红外亮温变化剧烈位置二值图像则可分别将TC中心密闭云区,螺旋云带和外散环流的边缘及梯度较大区域分割出来,这些区域是最后TC中心定位的主要依据。将上述两幅二值图像相与得到气旋主体云系红外亮温变化剧烈位置的二值图像,这一步剔除了TC的外散环流,而得到的二值图像便可分别将TC中心密闭云区和螺旋云带的边缘及梯度较大的区域分割出来。然后,对得到的气旋主体云系红外亮温变化剧烈位置二值图像进行Hough变换检测以减小气旋中心的搜索范围。最后,以检测区域内每个像素点为参考中心计算得到偏差角矩阵,并计算偏差角矩阵的方差填入对应检测区域内作为参考中心像素点的位置得到方差矩阵,将方差矩阵中值最小的位置作为气旋中心。因为TC除了少数特别强的时候大多数可以用圆形描述,而绝大多数时候TC要用螺旋线描述,但是具体是几度螺旋线来描述合适很难确定,本文用偏差角的方差就可以衡量这些云带、边缘的偏离状况是否集中,方差越小就表示偏离状况越集中。结果 运用该方法对400幅无眼TC红外图像和197幅有眼TC红外图像进行中心定位,分别与中国气象局（CMA）、日本气象厅（JMA）和美国台风预警中心（JTWC）的主观定位结果进行比较并取平均偏差,本文方法对有眼TC定位平均偏差约为27 km,无眼TC平均偏差约为45 km。具体到分别与CMA、JMA和JTWC的比较,对于有眼TC定位偏差分别为26.82 km,26.05 km和27.84 km,无眼TC定位偏差为45.84 km,44.84 km和47.15 km。结论 就结果而言,本文方法定位与CMA、JMA的偏差比较接近,与JTWC的偏差较大。就西北太平洋的TC而言,CMA和JMA的定位精度较高,JTWC精度稍低,这是与认知相符合,并且也证明了本文方法具有较高的可信度。此外,本文方法为TC定位提供了新的参考依据。     

Integrated order acceptance and supply chain scheduling problem with supplier selection and due date assignment

The importance, benefits, and impact of integration of decisions within supply chains have long been investigated by many researchers. Order acceptance and supplier selection are two of the most critical decisions for supply chain managers. Throughout the process of order acceptance, a manufacturer has to decide which orders to be accepted and processed and based on the accepted orders, the volume of required raw material is determined. On the other hand, a manufacturer aims to choose one or several suppliers among all possible choices to provide sufficient raw material for the accepted orders, subject to different criteria such as list price, transportation cost, etc. This paper addresses an integrated framework for profit maximization in an integrated supplier selection, order acceptance and scheduling problem in a single-machine environment with multiple customers. There is substantial literature on the problems of supplier selection and order acceptance; however, to the best of our knowledge, this paper is the first research that integrates these essential decisions in the form of a mathematical model to maximize the total profit. The problem is NP-hard in nature; therefore, solving to optimality is not practically possible for problems with medium and large size. For that purpose, we developed a Heuristic Algorithm (HA) to solve the problem above in a reasonable time, with proper accuracy. Results from this heuristic algorithm are compared with that of a commercial solver (GAMS) and the well-known Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). Computational experiments demonstrate that the developed heuristic algorithm is more efficient in comparison with other tested methods.     

结合最大方差比准则和PCNN模型的图像分割

脉冲耦合神经网络（PCNN）模型在图像分割方面有着很好的应用。在各项参数确定的情况下,其分割结果的好坏取决于循环迭代次数的多少,而PCNN模型自身无法实现迭代次数的自动判定。为此提出一种结合最大方差比准则的PCNN迭代次数自动判定算法,用于实现图像的自动分割。算法利用最大方差比准则找到图像的最优分割界限,确定PCNN的迭代次数,获得最优图像分割结果,然后利用最大香农熵准则验证分割结果。实验表明：提出的算法实现了PCNN迭代次数的自动判定,提高了PCNN的迭代速度,运行效率优于基于2D-OTSU和基于交叉熵的自动分割算法,图像分割效果良好。     

Structure identification of uncertain general complex dynamical networks with time delay

It is well known that many real-world complex networks have various uncertain information, such as unknown or uncertain topological structure and node dynamics. The structure identification problem has theoretical and practical importance for uncertain complex dynamical networks. At the same time, time delay often appears in the state variables or coupling coefficients of various practical complex networks. This paper initiates a novel approach for simultaneously identifying the topological structure and unknown parameters of uncertain general complex networks with time delay. In particular, this method is also effective for uncertain delayed complex dynamical networks with different node dynamics. Moreover, the proposed method can be easily extended to monitor the on-line evolution of network topological structure. Finally, three representative examples are then given to verify the effectiveness of the proposed approach.     

A novel method for structure selection of the Recurrent Random Neural Network using multiobjective optimisation

The Random Neural Network (RNN) has extensively investigated over the past few decades; this research has resulted in a considerable number of theoretical and application papers. Although, great effort has been done to develop a systematic procedure to train the recurrent fashion of the RNN, the choice of the number of neurons remains an open question. To overcome this problem, at least partially, this paper uses multiobjective optimisation (MOP) to select the number of neurons. The MOP framework used the mean square error (MSE) and the number of neurons (N) as the objectives to be minimised. The stochastic nondominated algorithm (SNA) to exclude dominated solutions of the Pareto-set has been also introduced. Instead of using only the best solution, candidates to the Pareto-set are excluded by statistical comparison among mean values of the two objectives in all training runs. The SNA allows a statistically correct exclusion of dominated solutions; the best solution can be picked up using classical decision-making procedures. Numerical and real examples illustrate the potentiality of the proposed method in two areas: classification problems and system identification.