An identification algorithm in fuzzy relational systems

The paper presents an effective identification method in fuzzy relational systems. We propose an algorithm for constructing models on the basis of fuzzy and nonfuzzy data with the aid of fuzzy discretization and clustering techniques. The usefulness of the method provided is demonstrated by means of two numerical examples. Also a possible way of generating a linguistic decision-making algorithm is discussed.     

Some remarks on the identification problem in fuzzy systems

Several identification problems in fuzzy systems are considered which are described by means of fuzzy relational equations. The determination of a family of fuzzy relations of the system is described in detail.     

Coordination through De Bruijn sequences

Let (x_t) be an n-periodic sequence in which the first n elements are drawn i.i.d. according to some rational distribution. We prove there exists a constant C such that whenever mlnm?Cn, with probability close to 1, there exists an automaton of size m that matches the sequence at almost all stages.     

A possibility measure is not a fuzzy measure

In this note we show that a possibility measure is not a particular type of fuzzy measure, except in trivial cases.     

Emprical distribution for linear system identification

Asymptotic properties of empirical distributions of approximate errors for least squares identification are developed in this work. As a preparation, it is first shown that a law of large numbers type of result holds for the empirical distribution. Then a scaled sequence is proved to converge to a Gaussian process with a Brownian bridge component. These results are useful for carrying out statistical inference tasks, goodness of fit tests, and related matters     

Parameterized (in)approximability of subset problems

We discuss approximability and inapproximability in FPT-time for a large class of subset problems where a feasible solution $S$ is a subset of the input data. We introduce the notion of intersective approximability that generalizes the one of safe approximability introduced in Guo et al. (2011) and show strong parameterized inapproximability results for many of the subset problems handled.     

A 2-approximation algorithm for the network substitution problem

The network substitution problem is to substitute an existing network for a new network so that to minimize the cost of exploiting the existing network during the period when the new network is being constructed. We show that this problem is NP-hard, and propose a 2-approximation algorithm for solving it.     

Two-machine flow shop scheduling problems with no-wait jobs

In this paper we provide a fairly complete complexity classification of various versions of the two-machine permutation flow shop scheduling problem to minimize the makespan in which some of the jobs have to be processed with no-wait in process. For some version, we offer a fully polynomial-time approximation scheme and a -approximation algorithm.     

Inner and outer fuzzy approximations of confidence intervals

We extend the notion of confidence region to fuzzy data, by defining a pair of fuzzy inner and outer confidence regions. We show the connection with previous proposals, as well as with recent studies on hypothesis testing with low quality data.     

Modelling and system identification of active magnetic bearing systems

Active magnetic bearing (AMB) systems have recently attracted much attention in the rotating machinery industry due to their advantages over traditional bearings such as fluid film and rolling element bearings. The AMB control system must provide robust performance over a wide range of machine operating conditions and over the machine lifetime in order to make this technology commercially viable. An accurate plant model for AMB systems is essential for the aggressive design of control systems. In this paper, we propose two approaches to obtain accurate AMB plant models for the purpose of control design: physical modelling and system identification. The former derives a model based upon the underlying physical principles. The latter uses input – output data without explicitly resorting to physical principles. For each problem, a brief summary of the theoretical derivation and assumptions is given. Experimental results based on data collected from an AMB test facility at the United Technologies Research Center provide a vehicle for a comparison of the two approaches.     

On identification in fuzzy systems and its applications in control problems

The problem of identification in fuzzy systems described by the use of fuzzy equation is considered. The identification method and its performance index is also presented. The formal procedure of the identification algorithm is illustrated by means of a numerical example. The possibility of using the proposed algorithm for the solution of a control problem is given as well.     

The bi-objective fuzzy c-means cluster analysis for TSK fuzzy system identification

For conventional fuzzy clustering-based approaches to fuzzy system identification, a fuzzy function is used for cluster formation and another fuzzy function is used for cluster validation to determine the number and location of the clusters which define IF parts of the rule base. However, the different fuzzy functions used for cluster formation and validation may not indicate the same best number and location of the clusters. This potential disparity motivates us to propose a new fuzzy clustering-based approach to fuzzy system identification based on the bi-objective fuzzy c-means (BOFCM) cluster analysis. In this approach, we use the BOFCM function for both cluster formation and validation to simultaneously determine the number and location of the clusters which we hope can efficiently and effectively define IF parts of the rule base. The proposed approach is validated by applying it to the truck backer-upper problem with an obstacle in the center of the field.     

Bounded max-colorings of graphs

In a bounded max-coloring of a vertex/edge weighted graph, each color class is of cardinality at most b and of weight equal to the weight of the heaviest vertex/edge in this class. The bounded max-vertex/edge-coloring problems ask for such a coloring minimizing the sum of all color classes' weights. These problems generalize the well known max-coloring problems by taking into account the number of available resources (i.e., the cardinality of color classes) in practical applications. In this paper we present complexity results and approximation algorithms for the bounded max-coloring problems on general graphs, bipartite graphs and trees.     

Horn函数可满足性的复杂性(英文)

本文证明了 Ｈｏｒｎ函数的极大可满足性即使是限制在如下两种情况中的任何一种也是 ＭＡＸ ＳＮＰ困难的,第一种情况是每个公式都是二次的,第二种是公式中每一个非单位子句有且只有一个补元,这意味着在这档两种情况下没有多项式的近似算法,除非Ｐ＝ＮＰ．     

Distinction between several subsets of fuzzy measures

In this paper, we place several fuzzy measure subsets in relation one with the other. The subsets under study are those corresponding to the definitions of probability measure. Sugeno's g_λ-measure, Shafer's belief function and Zadeh's possibility measure. We study the intersection of these subsets and we show the particular role of Dirac's measures in this comparison. We limit ourself to the case of mappins whose domain is the collection of all subsets of a finite set.Finally, the obtained partial results are summarized in only one figure which shoul clarify the specificity of each of the above definitions.     

Introducing validity in fuzzy probability for judicial decision-making

Since the Age of Enlightenment, most philosophers have associated reasoning with the rules of probability and logic. This association has been enhanced over the years and now incorporates the theory of fuzzy logic as a complement to the probability theory, leading to the concept of fuzzy probability. Our insight, here, is integrating the concept of validity into the notion of fuzzy probability within an extended fuzzy logic (FLe) framework keeping with the notion of collective intelligence. In this regard, we propose a novel framework of possibility–probability–validity distribution (PPVD). The proposed distribution is applied to a real world setting of actual judicial cases to examine the role of validity measures in automated judicial decision-making within a fuzzy probabilistic framework. We compute valid fuzzy probability of conviction and acquittal based on different factors. This determines a possible overall hypothesis for the decision of a case, which is valid only to a degree. Validity is computed by aggregating validities of all the involved factors that are obtained from a factor vocabulary based on the empirical data. We then map the combined validity based on the Jaccard similarity measure into linguistic forms, so that a human can understand the results. Then PPVDs that are obtained based on the relevant factors in the given case yield the final valid fuzzy probabilities for conviction and acquittal. Finally, the judge has to make a decision; we therefore provide a numerical measure. Our approach supports the proposed hypothesis within the three-dimensional contexts of probability, possibility, and validity to improve the ability to solve problems with incomplete, unreliable, or ambiguous information to deliver a more reliable decision.     

Maximum and minimum between fuzzy symbols in non-interactive and weakly non-interactive situations

We define fuzzy symbols as particular fuzzy sets whose membership functions operate between two linearly ordered spaces, and study the operations of maximum and of minimum between two fuzzy symbols. We consider the membership functions of the fuzzy symbols as possibility distributions. We study those of the maximum and the minimum of two non-interactive and weakly non-interactive variables.     

The minimum spanning tree problem with fuzzy costs

In this paper the minimum spanning tree problem in a given connected graph is considered. It is assumed that the edge costs are not precisely known and they are specified as fuzzy intervals. Possibility theory is applied to characterize the optimality of edges of the graph and to choose a spanning tree under fuzzy costs.