基于无失效数据的可靠度的估计

对二项分布无失效数据，在可靠度的先验密度为且时，给出了可靠度的多层Bayes估计．     

关于一种新模型对软件总体可靠度的估计

本文在对文[4]中软件可靠度的估计进行研究的基础上,提出了软件总体可靠性的一个新模型,对软件总体的可靠度进行了讨论,利用点估计方法,得到软件可靠度的点估计值,证明了软件总体失效率λ估计解的唯一性,并对缺陷数进行了估计,从而给出了新模型下软体总体可靠性的估计.     

Estimation of stress-strength reliability based on tail-modelling

In probabilistic design it is common to use parametric statistical models to calculate the probability of failure (P_f) obtained from stress-strength interference theory. In this respect, it is well known that P_f is sensitive to the shape of the upper and lower tails of the stress and strength distributions respectively and also to their interference. If the true shapes of these tails could be discovered, it would be possible to improve the existing techniques. However, in practice there is seldom sufficient real data available to establish these tails with statistical significance. Moreover, often one or both variables are expensive to sample. Thus a separate tail estimation may not always be possible. Motivated to define a theoretical safety factor, the author presents a method here which provides a general solution to the problem by considering the tail estimation for a sample of increased sized. This is obtained by a reformulation of the problem in terms of excesses over a threshold, and by breaking the analysis into two parts; estimation of the number of exceedances and modelling the excesses. The analysis is carried out by non-parametric estimation of the former and parametric modelling of the latter based on data from the critical region.     

Estimation of a structural linear regression model with a known reliability ratio

In this paper, we consider the estimation of the slope parameter of a simple structural linear regression model when the reliability ratio (Fuller (1987),Measurement Error Models, Wiley, New York) is considered to be known. By making use of an orthogonal transformation of the unknown parameters, the maximum likelihood estimator of and its asymptotic distribution are derived. Likelihood ratio statistics based on the profile and on the conditional profile likelihoods are proposed. An exact marginal posterior distribution of , which is shown to be at-distribution is obtained. Results of a small Monte Carlo study are also reported.The first author acknowledges partial finantial suport from CNPq-BRASIL.     

Estimation of system reliability for redundant stress-strength model

The reliability and an estimate for it are derived for series-parallel and parallel-series stress-strength model under assumption that all components are subjected to a common stress. We also obtain the asymptotic normal distribution of the estimate.     

Estimation of dependences based on Bayesian correction of a committee of classification algorithms

Estimation of dependence of a scalar variable on the vector of independent variables based on a training sample is considered. No a priori conditions are imposed on the form of the function. An approach to the estimation of the functional dependence is proposed based on the solution of a finite number of special classification problems constructed on the basis of the training sample and on the subsequent prediction of the value of the function as a group decision. A statistical model and Bayes’ formula are used to combine the recognition results. A generic algorithm for constructing the regression is proposed for different approaches to the selection of the committee of classification algorithms and to the estimation of their probabilistic characteristics. Comparison results of the proposed approach with the results obtained using other models for the estimation of dependences are presented.     

An intuitionistic fuzzy set based S$$^$$VM model for binary classification with mislabeled information

Traditionally, robust and fuzzy support vector machine models are used to handle the binary classification problem with noise and outliers. These models in general suffer from the negative effects of having mislabeled training points and disregard position information. In this paper, we propose a novel method to better address these issues. First, we adopt the intuitionistic fuzzy set approach to detect suspectable mislabeled training points. Then we omit their labels but use their full position information to build a semi-supervised support vector machine (\(\mathrm {S^3VM}\)) model. After that, we reformulate the corresponding model into a non-convex problem and design a branch-and-bound algorithm to solve it. A new lower bound estimator is used to improve the accuracy and efficiency for binary classification. Numerical tests are conducted to compare the performances of the proposed method with other benchmark support vector machine models. The results strongly support the superior performance of the proposed method.     

A multiobjective immune algorithm based on a multiple-affinity model

This paper presents a new multiobjective immune algorithm based on a multiple-affinity model inspired by immune system (MAM-MOIA). The multiple-affinity model builds the relationship model among main entities and concepts in multiobjective problems (MOPs) and multiobjective evolutionary algorithms (MOEAs), including feasible solution, variable space, objective space, Pareto-optimal set, ranking and crowding distance. In the model, immune operators including clonal proliferation, hypermutation and immune suppression are designed to proliferate superior antibodies and suppress the inferiors. MAM-MOIA is compared with NSGA-II, SPEA2 and NNIA in solving the ZDT and DTLZ standard test problems. The experimental study based on three performance metrics including coverage of two sets, convergence and spacing proves that MAM-MOIA is effective for solving MOPs.     

A classification rule reduction algorithm based on significance domains

Many rule systems generated from decision trees (like CART, ID3, C4.5, etc.) or from direct counting frequency methods (like Apriori) are usually non-significant or even contradictory. Nevertheless, most papers on this subject demonstrate that important reductions can be made to generate rule sets by searching and removing redundancies and conflicts and simplifying the similarities between them. The objective of this paper is to present an algorithm (RBS: Reduction Based on Significance) for allocating a significance value to each rule in the system so that experts may select the rules that should be considered as preferable and understand the exact degree of correlation between the different rule attributes. Significance is calculated from the antecedent frequency and rule frequency parameters for each rule; if the first one is above the minimal level and rule frequency is in a critical interval, its significance ratio is computed by the algorithm. These critical boundaries are calculated by an incremental method and the rule space is divided according to them. The significance function is defined for these intervals. As with other methods of rule reduction, our approach can also be applied to rule sets generated from decision trees or frequency counting algorithms, in an independent way and after the rule set has been created. Three simulated data sets are used to carry out a computational experiment. Other standard data sets from UCI repository (UCI Machine Learning Repository) and two particular data sets with expert interpretation are used too, in order to obtain a greater consistency. The proposed method offers a more reduced and more easily understandable rule set than the original sets, and highlights the most significant attribute correlations quantifying their influence on consequent attribute.     

Optimal allocation policies for testing-resource based on a software reliability growth model

A lot of development resources are consumed during the software testing phase fundamentally consisting of module testing, integration, testing and system testing. Then, it is of great importance for a manager to decide how to effectively spend testing-resources on software testing for developing a quality and reliable software.In this paper, we consider two kinds of software testing-resource allocation problems to make the best use of the specified testing-resources during module testing. Also, we introduce a software reliability growth model for describing the time-dependent behavior of detected software faults and testing-resource expenditures spent during the testing, which is based on a nonhomogeneous Poisson process. It is shown that the optimal allocation of testing-resources among software modules can improve software reliability.     

Estimation of system reliability in Brownian stress-strength models based on sample paths

Reliability of many stochastic systems depends on uncertain stress and strength patterns that are time dependent. In this paper, we consider the problem of estimating the reliability of a system when bothX(t) andY(t) are assumed to be independent Brownian motion processes, whereX(t) is the system stress, andY(t) is the system strength, at timet.This research was partially supported by the Air-Force Office of Scientific Research Grants AFOSR-89-0402 and AFOSR-90-0402.     

A cross entropy based algorithm for reliability problems

The Cross Entropy method has recently been applied to combinatorial optimization problems with promising results. This paper proposes a Cross Entropy based algorithm for reliability optimization of complex systems, where one wants to maximize the reliability of a system through optimal allocation of redundant components while respecting a set of budget constraints. We illustrate the effectiveness of the proposed algorithm on two classes of problems, software system reliability optimization and complex network reliability optimization, by testing it on instances from the literature as well as on randomly generated large scale instances. Furthermore, we show how a Cross Entropy-based algorithm can be fine-tuned by using a training scheme based upon the Response Surface Methodology. Computational results show the effectiveness as well as the robustness of the algorithm on different classes of problems.     

Multiple kernel boosting framework based on information measure for classification

The performance of kernel-based method, such as support vector machine (SVM), is greatly affected by the choice of kernel function. Multiple kernel learning (MKL) is a promising family of machine learning algorithms and has attracted many attentions in recent years. MKL combines multiple sub-kernels to seek better results compared to single kernel learning. In order to improve the efficiency of SVM and MKL, in this paper, the Kullback–Leibler kernel function is derived to develop SVM. The proposed method employs an improved ensemble learning framework, named KLMKB, which applies Adaboost to learning multiple kernel-based classifier. In the experiment for hyperspectral remote sensing image classification, we employ feature selected through Optional Index Factor (OIF) to classify the satellite image. We extensively examine the performance of our approach in comparison to some relevant and state-of-the-art algorithms on a number of benchmark classification data sets and hyperspectral remote sensing image data set. Experimental results show that our method has a stable behavior and a noticeable accuracy for different data set.     

Quiz games as a model for information hiding

We present a general computation model inspired in the notion of information hiding in software engineering. This model has the form of a game which we call quiz game. It allows in a uniform way to prove exponential lower bounds for several complexity problems.     

A new structured quasi-Newton algorithm using partial information on Hessian

This paper presents a modified quasi-Newton method for structured unconstrained optimization. The usual SQN equation employs only the gradients, but ignores the available function value information. Several researchers paid attention to other secant conditions to get a better approximation of the Hessian matrix of the objective function. Recently Yabe et al. (2007) [6] proposed the modified secant condition which uses both gradient and function value information in order to get a higher-order accuracy in approximating the second curvature of the objective function. In this paper, we derive a new progressive modified SQN equation, with a vector parameter which use both available gradient and function value information, that maintains most properties of the usual and modified structured quasi-Newton methods. Furthermore, local and superlinear convergence of the algorithm is obtained under some reasonable conditions.     

Test of a new bosonization algorithm for a simple one-dimensional model

Different aspects of a new bosonization algorithm are tested by numerical simulations of a simple one-dimensional model. Translated from Teoreticheskaya i Matematicheskaya Fizika, Vol. 113, No. 1, pp. 58–67, October, 1997.     

基于新的拟牛顿方程的Broyden-Fletcher-Goldfarb-Shanno算法

通过对函数的泰勒展开式进行误差分析,提出了对二次模型进行改进的新模型,在此基础上得到了改进的拟牛顿条件,并得到了与其相应的Broyden-Fletcher-Goldfarb-Shanno(BFGS)算法.证明了在适当条件下该算法全局收敛.从试验函数库中选择标准测试函数,对经典的BFGS算法与改进的BFGS算法进行数值试验,试验结果表明改进的算法优于经典的BFGS算法.     

On a new stochastic global optimization algorithm based on censored observations

In this paper a new algorithm is proposed for global optimization problems. The main idea is that of modifying a standard clustering approach by sequentially sampling the objective function while adaptively deciding an appropriate sample size. Theoretical as well as computational results are presented.     

基于混合高斯过程模型的高光谱图像分类算法

提出了一种基于混合高斯过程模型的高光谱遥感图像分类算法,它不同于传统的基于多元统计的分类方法.为更好利用高光谱遥感图像的高谱分辨率特点,首先将函数数据分析的思想引进高光谱数据的分类问题,分类对象视为像元对应的谱线,故它们是函数型数据.为了有效模拟地物在空间上的分片聚集特性,则将混合高斯分布模型推广到混合高斯过程模型并用于高光谱数据分类算法中.数值实验表明,混合高斯过程模型是处理函数型数据的有效方法.