CONFLICT RESOLUTION IN THE FUSION OF FUZZY KNOWLEDGE VIA INFORMATION MAXIMIZATION

The measure of specificity is introduced and shown to be an appropriate measure for the amount of information contained in a fuzzy subset. We then turn to fusion of fuzzy observations. It is shown that if the data being fused is non-conflicting then the maximal information is obtained by simply taking the intersection of the fuzzy observations. When the data is conflicting the use of the intersection can result in a fused value having less information then any of its components. In order to increase the information content in this conflicting environment some meta-knowledge must be introduced to adjudicate between conflicting data. Two approaches to address this problem are introduced. The first approach considers the possibility of using only a subset of the observations to construct the fused value. The basic rational of this approach is to calculate the fused value from a subset of observations that are not to conflicting and consisted of enough of the observations to be considered a credible fusion. Central to this approach is the introduction of meta-knowledge in the form of a measure of credibility associated with the use of different subsets of the observations. The second approach is based upon the introduction of a prioritization of the observations. In this approach an observation is essentially discounted if conflicts with higher priority observations.     

一种基于模糊Gibbs随机场的运动估计新算法

运动估计问题具有不适定性，单纯采用最大后验概率算法，实际上并未解决运动矢量的不连续、矢量的失真与随机噪声等棘手问题。本文应用模糊数据融合与Gibbs分布的基本思想，将运动场风险约束条件的概率分布模式有效地纳入阶段非凸函数(GNC)算法的局部迭代过程中，从而提高了运动估计效果。首先建立Gibbs的自适应能量模型，该模型可将基于特征和基于梯度的两类矢量按照优化约束条件进行融合；其次利用各种运动经验知识构造矢量的模糊风险决策表，该决策表可对Gibbs能量方程的每一步迭代收敛结果进行监督和修正，从而实现模糊数据融合。从收敛性和鲁棒性两方面说，模糊融合后的结果在原有基础上有明显提高。     

Fuzzy hierarchical data fusion networks for terrain location identification problems.

The terrain location identification problem represents a very complicated learning task. Beside of learning from noisy and nondeterministic training data, the training task must learn from a very large size of training data, which may lead to lots of learning problems. A phenomenon called the fake convergence is observed in our implementation. In that case, the training process seemed to converge to a fixed error level, but the actual error is much higher than the converged one. In our study, a fuzzy hierarchical network is proposed to cope with the problem of large training data sets. With this fuzzy hierarchical structure, the learning process can become fast and errors are significantly reduced. Another issue is regarding about embedding domain knowledge into the learning structure of neural fuzzy networks. The idea is simple but effective. The proposed structure is called the fuzzy hierarchical data fusion network and its learning performance is significantly better than that of original fuzzy hierarchical networks. With the use of fuzzy hierarchical data fusion networks, errors indeed can converge and the system becomes practically applicable.     

Fusing object information and peer information

We consider the problem of information fusion, specifically the task of fusing information from two different categories, information which is directly about an object of interest (OBJOIN information) and information about related objects (peer information). We discuss the representation of these different types of information, the first in terms of a possibilistic distribution and the second in terms of a probability distribution. We introduce an approach to information fusion based upon the use of the fuzzy modeling technology. In this approach we represent the fusion function in terms of rules which indicate when to use the different types of information. Particularly notable here is the role of information quality as a guiding factor in the fusion process. © 2001 John Wiley & Sons, Inc.     

Membership modification and level sets

We introduce the concept of a membership modification function and describe its role in transforming one fuzzy set into another. We discuss the related idea of a membership modification program consisting of a collection of related membership modification functions and show how the concept of level sets is an example of a membership modification program. Using this idea of membership modification allows us to consider other transformations of fuzzy sets that soften the idea of level sets. Using these ideas we provide an extension of the Jaccard similarity index.     

A new fusion strategy for web metasearch

In this paper, we present two new list fusion strategies for the collection fusion problem in web metasearch. The new approaches fall in the category of isolated methods since they consider as input the lists obtained by different search engines only, plus a fuzzy degree of relevance in one of the methods. In the latter case, we employ a recently proposed approach for the representation of fuzzy preferences, namely, RL-preference relations. A remarkable contribution of the approaches is that the result of the fusion is an ordered list of indistinguishable groups of documents. This kind of output represents a good compromise between understandability and accuracy of the result. We have illustrated the performance of the method by means of several experiments and a comparison with other web metasearchers.     

A new methodology of fuzzy constraint-based controller design viaconstraint-network processing

A complete design framework for a fuzzy constraint-based controller based on fuzzy-constraint processing and its semantics and relationship to fuzzy logic is presented. In this paper, the concept of “fuzzy constraints” in problem solving is introduced, and some basic definitions of fuzzy-constraint processing in a constraint network and its semantic modeling are addressed. Then a fuzzy local propagation inference mechanism for reasoning about imprecise information applying the filter operation in a network of constraints is proposed. Moreover, we advance the concurrent fuzzy-logic controller (FLC) to a new type of controller, the fuzzy constraint-based controller (FCC), using a more general predicate calculus and full first-order logic knowledge representation and making use of the idea of fuzzy-constraint processing to model practical dynamic control systems. Finally, simulation results show that a FCC achieves equivalent performance as PD type and PI type FLCs and it also demonstrates superior outcomes to a conventional PID controller in terms of rise time and peak-percent overshoot     

Possibilistic Information Fusion Using Maximal Coherent Subsets

When multiple sources provide information about the same unknown quantity, their fusion into a synthetic interpretable message is often a tricky problem, especially when sources are conflicting. In this paper, we propose to use possibility theory and the notion of maximal coherent subsets (MCSs), often used in logic-based representations, to build a fuzzy belief structure that will be instrumental both for extracting useful insight about various features of the information conveyed by the sources and for compressing this information into a unique possibility distribution. Extensions and properties of the basic fusion rule are also studied.      

Choquet integrals of weighted intuitionistic fuzzy information

The Choquet integral is a very useful way of measuring the expected utility of an uncertain event [G. Choquet, Theory of capacities, Annales de l’institut Fourier 5 (1953) 131-295]. In this paper, we use the Choquet integral to propose some intuitionistic fuzzy aggregation operators. The operators not only consider the importance of the elements or their ordered positions, but also can reflect the correlations among the elements or their ordered positions. It is worth pointing out that most of the existing intuitionistic fuzzy aggregation operators are special cases of our operators. Moreover, we propose the interval-valued intuitionistic fuzzy correlated averaging operator and the interval-valued intuitionistic fuzzy correlated geometric operator to aggregate interval-valued intuitionistic fuzzy information, and apply them to a practical decision-making problem involving the prioritization of information technology improvement projects.     

Linguistic hesitant intuitionistic fuzzy decision-making method based on VIKOR

Linguistic hesitant intuitionistic fuzzy set, which allows an element having several linguistic evaluation values and each linguistic argument having several intuitionistic fuzzy memberships, is a power tool to model uncertain information existing in multiple attribute decision-making problems. In this paper, we propose new methods by using TOPSIS and VIKOR for multiple attribute decision-making problems, in which evaluation values are in the form of linguistic hesitant intuitionistic fuzzy elements. Different situations of attribute weight information are considered. If attribute weights are partly known, a linear programming model is set up based on the idea that reasonable weights should make the relative closeness of each alternative evaluation value to the linguistic hesitant intuitionistic fuzzy positive ideal solution as large as possible. If attribute weights are unknown completely, an optimization model is set up based on the maximum deviation method. A numerical example is presented to illustrate feasibility and practical advantages of the proposed method. We compare the alternatives’ rankings derived from the linguistic hesitant intuitionistic fuzzy TOPSIS method with those derived from the hesitant fuzzy linguistic TOPSIS and the hesitant intuitionistic fuzzy TOPSIS approach to further illustrate their advantages.

     

面向后件集的模糊推理机制: 用于区间型type-2模糊逻辑系统

目前,大多数模糊推理都是利用t-范数和t-余范数或其改进形式对连接词进行建模,这些模型不能将模糊规则中前件集与后件集之间的相关性信息引入到模糊推理过程,这会丢失蕴含在规则中的一些信息甚至导致推理结果与实际经验严重不符.为解决此问题,本文首先引入模糊集合面向对象变换的概念,并将其推广,建立了合成type-2模糊集合模型.基于此模型,针对区间型type-2模糊逻辑系统,提出一种面向后件集的模糊推理机制,该机制能将前件集与后件集的相关性信息(包括清晰数和模糊数两种情形)引入到模糊推理过程.仿真结果表明,该方法能捕获到模糊规则中更多的不确定性信息,并为模糊逻辑系统的设计提供更大的自由度.     

On the fuzzy cardinality of a fuzzy set

We look at problem of determining the number of elements of a fuzzy set and its cardinality. It is noted that the simple adding of membership grades often does not lead to a satisfactory results. We investigate the use of a fuzzy subset of the non-negative integers to represent the cardinality of a fuzzy set. We prove a fundamental relationship between the degree of fuzziness of a set A and the specificity of the fuzzy subset representing its cardinality. The problem of determining whether the cardinality of a fuzzy set satisfies a given linguistic expressed quantity is also investigated     

一种融合RST与模糊聚类的故障诊断方法

针对模糊聚类方法中存在冗余信息的问题,提出一种融合粗糙集属性约简和模糊等价关系的故障诊断方法,通过应用粗糙集属性约简算法对冗余数据的处理后再应用模糊等价关系聚类获取聚类结果。该方法与单采用模糊等价关系聚类法相比,不仅能够有效减少模糊等价关系矩阵求解中的迭代次数,而且获得聚类数也得到有效降低,并通过实例验证了该方法的有效性和高效性。     

Design and analysis of fuzzy schedulers using fuzzy Lyapunov synthesis

Recently, a new approach to the design of fuzzy control rules was suggested. The method, referred to as fuzzy Lyapunov synthesis, extends classical Lyapunov synthesis to the domain of “computing with words”, and allows the systematic, instead of heuristic, design and analysis of fuzzy controllers given linguistic information about the plant. In this paper, we use fuzzy Lyapunov synthesis to design and analyze the rule-base of a fuzzy scheduler. Here, too, rather than use heuristics, we can derive the fuzzy rule-base systematically. This suggests that the process of deriving the rules can be automated. Our approach may lead to a novel computing with words algorithm: the input is linguistic information concerning the “plant” and the “control” objective, and the output is a suitable fuzzy rule-base.     

Ranking of System Elements Based on Fuzzy Relation of Influence and Transitive Closure

The author proposes a method of elements ranking in multi-functional system using the fuzzy relations theory. The problem is formulated as an automatic classification based on the transitive closure of the fuzzy similarity relation. The original information about the system is given as a fuzzy relation of influence of elements’ failures on the performance of system’s functions. To calculate the degrees of element’s influence on the functions performance, we use the comparison of all influences with the least influence by 9-point Saaty scale. The proposed method relaxes the assumption about the independence and the binary-state (up-down) of elements. The possible fields of application are multi-functional systems with ill-defined structures such as organizational, ergatic, military, etc.     

Decision making with fuzzy probability assessments

We discuss the idea of a fuzzy probability assessment, the association a collection of fuzzy probabilities with the outcomes of a random experiment. Fuzzy probability assessments often result from the linguistic specification of probabilities as provided by human experts. The question of consistency of the fuzzy probability assessment is considered. Finally, the problem of decision-making, selecting a best alternative action, in the face of a fuzzy probability assessment is investigated. Here we focus on the issue of obtaining the expected payoff of alternatives in the face of a fuzzy probability assessment. In the course of solving this problem we develop a representation of an effective probability distribution in the face of a fuzzy probability assessment     

Planning routes through uncertain territory

Planning routes and executing them requires both topological and metric information. A natural implementation of a ‘cognitive map’ might therefore consist of an assertional data base for topological information and a ‘fuzzy map’ for the metric information. A fuzzy map captures facts about objects by recording their relative positions, orientations, and scales in convenient frames of reference. It is fuzzy in the sense that coordinates are specified to lie in a range rather than having fixed values. The fuzzy map allows easy retrieval of information. The same information is also represented in a discrimination tree, which allows an object to be retrieved given its location and other attributes. The problem of constructing a fuzzy map is more difficult; we present a partial solution, an algorithm that assimilates a fact first by imposing constraints on the fuzzy coordinates of the objects involved, then by rearranging or growing the tree of frames of reference. Route planning is modelled as a process of finding the overall direction and topology of the path, then filling in the details by deciding how to go around barriers. It uses the retrieval algorithms. Our program SPAM carries out all these processes.     

A geometric framework for data fusion in information retrieval

Data fusion in information retrieval has been investigated by many researchers and a number of data fusion methods have been proposed. However, problems such as why data fusion can increase effectiveness and favorable conditions for the use of data fusion methods are poorly resolved at best. In this paper, we formally describe data fusion under a geometric framework, in which each component result returned from an information retrieval system for a given query is represented as a point in a multi-dimensional space. The Euclidean distance is the measure by which the effectiveness and similarity of search results are judged. This allows us to explain all component results and fused results using geometrical principles. In such a framework, score-based data fusion becomes a deterministic problem. Several interesting features of the centroid-based data fusion method and the linear combination method are discussed. Nevertheless, in retrieval evaluation, ranking-based measures are the most popular. Therefore, this paper investigates the relation and correlation between the Euclidean distance and several typical ranking-based measures. We indeed find that a very strong correlation exists between these. It means that the theorems and observations obtained using the Euclidean distance remain valid when ranking-based measures are used. The proposed framework enables us to have a better understanding of score-based data fusion and use score-based data fusion methods more precisely and effectively in various ways.     

Learning common behaviors from large sets of unlabeled temporal series

This paper is about extracting knowledge from large sets of videos, with a particular reference to the video-surveillance application domain. We consider an unsupervised framework and address the specific problem of modeling common behaviors from long-term collection of instantaneous observations. Specifically, such data describe dynamic events and may be represented as time series in an appropriate space of features. Starting off from a set of data meaningful of the common events in a given scenario, the pipeline we propose includes a data abstraction level, that allows us to process different data in a homogeneous way, and a behavior modeling level, based on spectral clustering. At the end of the pipeline we obtain a model of the behaviors which are more frequent in the observed scene, represented by a prototypical behavior, which we call a cluster candidate. We report a detailed experimental evaluation referring to both benchmark datasets and on a complex set of data collected in-house. The experiments show that our method compares very favorably with other approaches from the recent literature. In particular the results we obtain prove that our method is able to capture meaningful information and discard noisy one from very heterogeneous datasets with different levels of prior information available.     

Importance-based multicriteria decision making with interval valued criteria satisfactions

Multiple-criteria decision problems involve selecting an alternative that best satisfies a collection of criteria as quantified by a scalar corresponding to an aggregation of the alternatives satisfaction to the individual criteria. A fundamental issue is the formulation of decision maker's aggregation function based upon the decision maker's perceived relationship between the criteria. Here, we allow the decision maker to express their perceived relationship between the criteria in terms of information about the criteria importances by providing a fuzzy measure over the criteria such that the measure of any subset of criteria is its importance. With the aid of the Choquet integral, we use this fuzzy measure of importances to construct an aggregation function. As the Choquet integral requires an ordering of an alternatives individual criteria satisfactions, special handling is required in the case when criteria satisfactions are interval valued rather then scalar. Here we use the golden rule representative value in the case of interval values.