Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints

Independent factor analysis (IFA) defines a generative model for observed data that are assumed to be linear mixtures of some unknown non-Gaussian, mutually independent latent variables (also called sources or independent components). The probability density function of each individual latent variable is modelled by a mixture of Gaussians. Learning in the context of this model is usually performed within an unsupervised framework in which only unlabelled samples are used. Both the mixing matrix and the parameters of latent variable densities are learned from the observed data. This paper investigates the possibility of estimating an IFA model in a noiseless setting when two kinds of prior information are incorporated, namely constraints on the mixing process and partial knowledge on the cluster membership of some training samples. Semi-supervised or partially supervised learning frameworks can thus be handled. The investigation of these two kinds of prior information was motivated by a real-world application concerning the fault diagnosis of railway track circuits. Simulated data, resulting from both these applications, are provided to demonstrate the capacity of our approach to enhance estimation accuracy and remove the indeterminacy commonly encountered in unsupervised IFA, such as source permutations.     

Application of fuzzy Naive Bayes and a real-valued genetic algorithm in identification of fuzzy model

We present a method to identify a fuzzy model from data by using the fuzzy Naive Bayes and a real-valued genetic algorithm. The identification of a fuzzy model is comprised of the extraction of “if–then” rules that is followed by the estimation of their parameters. The involved parameters include those which determine the membership function of fuzzy sets and the certainty factors of fuzzy if–then rules. In our method, as long as the fuzzy partition in the input–output space is given, the certainty factor of each rule is computed with the fuzzy conditional probability of the consequent conditioned on the antecedent by using the fuzzy Naive Bayes, which is a generalization of Naive Bayes. The fuzzy model involves the rules characterized by the highest values of certainty factors. The certainty factor of each rule is the fuzzy conditional probability, and it reflects the inner relationship between the antecedent and the consequent. In order to improve the accuracy of the fuzzy model, the real-valued genetic algorithm is incorporated into our identification process. This process concerns the optimization of the membership functions occurring in the rules. We just involve the parameters of membership function of the fuzzy sets into the real-valued genetic algorithm, since the certainty factor of each rule can be computed automatically. The performance of the model is shown for the backing-truck problem and the prediction of Mackey–Glass time series.     

一种改进的自适应模糊卡尔曼滤波算法

针对常规卡尔曼滤波（KF）处理小噪声和变化噪声不足,提出了一种改进的自适应模糊卡尔曼滤波[1]（IAF-KF）算法。该算法根据模糊推理输入量的变化特点建立一个新的非线性隶属度函数,取代了常用的三角形线形隶属度函数;然后利用模糊化后的等级和隶属度构造了补偿调节函数（CAF）,用于调节卡尔曼滤波算法中的误差,提高实际测量误差与理论测量误差间的匹配程度。仿真实验表明,较之传统的卡尔曼滤波,该方法在小噪声和变化的噪声情形下有效的克服了稳态误差,同时降低了模糊卡尔曼滤波算法的复杂程度。     

基于MRF模型的鲁棒FCM分割算法

FLICM算法是一种基于FCM框架的有效的分割方法。然而,它对于强噪声图像的分割仍然不够准确。本文使用MRF模型的局部先验概率,对FLICM算法从两方面进行了改进。首先,在计算模糊因子时,使用先验概率对距离函数进行加权。改进的模糊因子考虑了更大范围的邻域约束,从而使算法受噪声的影响程度减弱。其次,在分割阶段,进一步使用局部先验概率对FLICM算法的隶属度进行加权。使用改进后的隶属度进行标记判决,使得每一标记的确定需要考虑邻域标记的影响,使分割结果的区域性更好。利用新算法对模拟影像和真实影像进行了分割实验,并与几个考虑空间信息约束的FCM分割算法进行了对比分析,结果证明该算法具有更强的抗噪性能。     

基于遗传算法的污水处理模糊控制方法

模糊控制中的模糊推理规则和隶属函数的选取往往依据相关专家或技术人员的实际经验,对具有较强的非线性系统和未知动态环境条件下,其控制性能往往达不到很好的效果.使用遗传算法同时对隶属函数和模糊规则进行优化,从而使模糊推理规则和隶属函数的确定摆脱了人为经验的局限,提高了模糊控制的自适应能力.在此基础上设计出模糊控制器,并将其应用于污水处理溶解氧的控制中.实验结果表明,该控制器能够使溶解氧快速、准确地达到期望的要求.     

A stratified framework for handling conditional preferences: An extension of the analytic hierarchy process

Representing and reasoning over different forms of preferences is of crucial importance to many different fields, especially where numerical comparisons need to be made between critical options. Focusing on the well-known Analytical Hierarchical Process (AHP) method, we propose a two-layered framework for addressing different kinds of conditional preferences which include partial information over preferences and preferences of a lexicographic kind. The proposed formal two-layered framework, called CS-AHP, provides the means for representing and reasoning over conditional preferences. The framework can also effectively order decision outcomes based on conditional preferences in a way that is consistent with well-formed preferences. Finally, the framework provides an estimation of the potential number of violations and inconsistencies within the preferences. We provide and report extensive performance analysis for the proposed framework from three different perspectives, namely time-complexity, simulated decision making scenarios, and handling cyclic and partially defined preferences.     

分簇结构谓词以及编译优化框架研究

谓词执行是在控制流存在的条件下可以有效挖掘指令级并行性的硬件机制。而在分簇结构上实现谓词机制,可以提高分簇结构上条件的执行效率。本文针对分簇结构展开谓词体系体系结构的研究,提出了分簇结构部分谓词的高效实现方法,以及基于循环展开的分簇结构部分谓词支持框架。实验表明,本文提出的分簇结构部分谓词及编译框架可以很好地提高条件执行程序的执行效率。     

A probabilistic framework for real-time performance assessment of inferential sensors

A definition for the reliability of inferential sensor predictions is provided. A data-driven Bayesian framework for real-time performance assessment of inferential sensors is proposed. The main focus is on characterizing the effect of operating space on the reliability of inferential sensor predictions. A holistic, quantitative measure of the reliability of the inferential sensor predictions is introduced. A methodology is provided to define objective prior probabilities over plausible classes of reliability based on the total misclassification cost. The real-time performance assessment of multi-model inferential sensors is also discussed. The application of the method does not depend on the identification techniques employed for model development. Furthermore, on-line implementation of the method is computationally efficient. The effectiveness of the method is demonstrated through simulation and industrial case studies.     

A Thorough Axiomatization of a Principle of Conditional Preservation in Belief Revision

Although the crucial role of if-then-conditionals for the dynamics of knowledge has been known for several decades, they do not seem to fit well in the framework of classical belief revision theory. In particular, the propositional paradigm of minimal change guiding the AGM-postulates of belief revision proved to be inadequate for preserving conditional beliefs under revision. In this paper, we present a thorough axiomatization of a principle of conditional preservation in a very general framework, considering the revision of epistemic states by sets of conditionals. This axiomatization is based on a nonstandard approach to conditionals, which focuses on their dynamic aspects, and uses the newly introduced notion of conditional valuation functions as representations of epistemic states. In this way, probabilistic revision as well as possibilistic revision and the revision of ranking functions can all be dealt with within one framework. Moreover, we show that our approach can also be applied in a merely qualitative environment, extending AGM-style revision to properly handling conditional beliefs.     

A possibilistic approach to clustering

The clustering problem is cast in the framework of possibility theory. The approach differs from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function. The advantages of the resulting family of possibilistic algorithms are illustrated by several examples     

Self-Conditional Probabilities and Probabilistic Interpretations of Belief Functions

We present an interpretation of belief functions within a pure probabilistic framework, namely as normalized self-conditional expected probabilities, and study their mathematical properties. Interpretations of belief functions appeal to partial knowledge. The self-conditional interpretation does this within the traditional probabilistic framework by considering surplus belief in an event emerging from a future observation, conditional on the event occurring. Dempster's original interpretation, in contrast, involves partial knowledge of a belief state. The modal interpretation, currently gaining popularity, models the probability of a proposition being believed (or proved, or known). The versatility of the belief function formalism is demonstrated by the fact that it accommodates very different intuitions.     

基于常量条件函数依赖的冲突消解算法

目前绝大部分冲突消解方法都是基于迭代计算数据源可靠度和事实可信度的机制。当数据源较少时,数据源的可靠度难于进行评估,仅凭投票来消解冲突往往会造成较大误差。针对数据源较少时的冲突消解问题,提出基于常量条件函数依赖的冲突消解算法。根据多个数据源之间的冲突,找出冲突匹配对及对应的冲突候选值集合。考虑常量条件函数依赖中具体到部分实例子集的约束关系,将常量条件函数依赖集作为先验知识,通过判断候选值是否符合常量条件函数依赖来选择正确的候选值,避免了错误数据比例较大时直接投票选择产生的误差。通过两个真实数据集上的对比实验验证了上述算法的有效性。     

Pursuing a maneuvering target which uses a random process for itscontrol

Since a pursuer pursuing a maneuvering target does not know what maneuvers an evading target will make, the maneuvers (the target's control law) appear as a random process to the pursuer. However, he has opinions about what the evader will do. From these, he can assign a prior probability distribution to the evader's maneuvers. For a linear pursuit evasion problem in which the evader's control law is modeled as a random process, in which the pursuer has partial noisy linear measurements of his own and the evader's relative position, and a quadratic optimality criterion is used, past results of the authors imply that the optimal control is a linear function of the “predicted miss”. Determining the predicted miss involves estimating the evader's terminal position from past system measurements. Nonlinear filtering techniques are used to give expressions for computing the conditional expectation of the evader's terminal position even in the presence of the random unknown maneuvers of the evader     

Hyperspectral Imagery Denoising Using a Spatial-Spectral Domain Mixing Prior

By introducing a novel spatial-spectral domain mixing prior,this paper establishes a Maximum a posteriori (MAP) framework for hyperspectral images (HSIs) denoising.The proposed mixing prior takes advantage of different properties of HSI in the spatial and spectral domain.Furthermore,we propose a spatially adaptive weighted prior combining smoothing prior and discontinuity-preserving prior in the spectral domain.The weights can be defined as a function of the spectral discontinuity measure (DM).For minimizing the objective function,a half-quadratic optimization algorithm is used.The experimental results illustrate that our proposed model can get a higher signal-to-noise ratio (SNR) than using only smoothing prior or discontinuity-preserving prior.     

An inferential approach to Information Retrieval and its implementation using a manual thesaurus

Most inferential approaches to Information Retrieval (IR) have been investigated within the probabilistic framework. Although these approaches allow one to cope with the underlying uncertainty of inference in IR, the strict formalism of probability theory often confines our use of knowledge to statistical knowledge alone (e.g. connections between terms based on their co-occurrences). Human-defined knowledge (e.g. manual thesauri) can only be incorporated with difficulty. In this paper, based on a general idea proposed by van Rijsbergen, we first develop an inferential approach within a fuzzy modal logic framework. Differing from previous approaches, the logical component is emphasized and considered as the pillar in our approach. In addition, the flexibility of a fuzzy modal logic framework offers the possibility of incorporating human-defined knowledge in the inference process. After defining the model, we describe a method to incorporate a human-defined thesaurus into inference by taking user relevance feedback into consideration. Experiments on the CACM corpus using a general thesaurus of English, Wordnet, indicate a significant improvement in the system's performance.     

Ordinal fuzzy sets

Fuzzy set theory has been used as a framework for interpreting imprecise linguistic expressions. In general, a linguistic term is described by the compatibility ordering induced in some universe of discourse (UoD). A membership function in fuzzy set theory serves to reflect this ordering by assignment of values in [0, 1] for objects in UoD. When we compute the meaning of a linguistic expression such as "young and tall" using fuzzy membership functions, two implicit assumptions are made. First, we assume the membership values have quantitative meaning so that they can be quantitatively manipulated, for example, by adding or subtracting (the extensive scale assumption). Second, we assume that the scales of the membership values used in describing the different linguistic terms are comparable and the same (the common scale assumption). In many cases, these assumptions cannot be justified. Some proposals have been made to address the first issue by using ordinal scale in defining fuzzy membership functions. However, the second issue has not been properly investigated. In this paper, we propose a framework that does not depend on both of these assumptions. Such framework will facilitate our understanding and investigation of qualitative reasoning without the extensive scale and common scale assumptions.     

Credit scoring analysis using a fuzzy probabilistic rough set model

Credit scoring analysis is an important activity, especially nowadays after a huge number of defaults has been one of the main causes of the financial crisis. Among the many different tools used to model credit risk, the recent development of rough set models has proved effective. The original development of rough set theory has been widely generalized and combined with other approaches to uncertain reasoning, especially probability and fuzzy set theories. Since coherent conditional probability assessments cope well with the problem of unifying these different approaches, a merging of fuzzy rough set theory with this subjectivist approach is proposed. Specifically, expert partial probabilistic evaluations are encompassed inside a gradual decision rule structure, with coherence of the conclusion as a guideline. In line with Bayesian rough set models, credibility degrees of multiple premises are introduced through conditional probability assessments. Nonetheless, discernibility with this method remains too fine. Therefore, the basic partition is coarsened by equivalence classes based on the arity of positively, negatively and neutrally related criteria. A membership function, which grades the likelihood of default, is introduced by a peculiar choice of t-norms and t-conorms. To build and test the model, real data related to a sample of firms are used.     

Linguistic models as a framework of user-centric system modeling

In this paper, the fundamental idea of linguistic models introduced by Pedrycz and Vasilakos (1999) is followed and their comprehensive design framework is developed. The paradigm of linguistic modeling is concerned with constructing models that: 1) are user centric and 2) inherently dwell upon collections of highly interpretable and user-oriented entities such as information granules. The objective of this paper is to investigate and compare alternative design options, present an organization of the overall optimization process, and come up with a specification of several evaluation mechanisms of the performance of the models. The underlying design tool guiding the development of linguistic models revolves around the augmented version of fuzzy clustering known as a context-based or conditional fuzzy C-means (C-FCM). The design process comprises several main phases such as: 1) defining and further refining context fuzzy sets; 2) completing conditional fuzzy clustering; and 3) optimizing parameters (connections) linking information granules in the input and output spaces. An iterative process of forming information granules in the input and output spaces is discussed. Their membership functions are adjusted by the gradient-based learning guided by the minimization of some performance index. The paper comes with a comprehensive suite of experiments that lead to some design guidelines of the models. Furthermore, the performance of linguistic models is contrasted with that of other fuzzy models, especially radial basis function neural networks (RBFNNs) and related constructs that are based on concepts of fuzzy clustering.     

On worst-case approximation of feasible system sets via orthonormal basis functions

This note deals with the approximation of sets of linear time-invariant systems via orthonormal basis functions. This problem is relevant to conditional set membership identification, where a set of feasible systems is available from observed data, and a reduced-complexity model must be estimated. The basis of the model class is made of impulse responses of linear filters. The objective of the note is to select the basis function poles according to a worst-case optimality criterion. Suboptimal conditional identification algorithms are introduced and tight bounds are provided on the associated identification errors.