House of Quality

R. Babuska, Fuzzy Modeling for Control, Kluwer Academic Publ., Dordrecht, 1998, 288 p. B. Bouchon-Meunier, L. Foulloy, M. Ramdani, Logique Floue Exercices Corriges et Exemples d' Applications, Cepadues Editions. Toulouse, 199B, 200 p. G.Q. Chen, Fuzzy Logic in Data Modeling Semantics, Constraints and Database Design, Kluwer Academic Publ., Boston. 1998, 240 p. K.J. Cios. W. Pedrycz, R.S. Swiniarski. Data Mining Methods for Knowledge Discovery, Kluwer Academic Publ., Dordrecht. 199B. J. Godjevac, Idees Nettes sur la Logique Floue, Presses Polytechniques et Universitaires Romandes. Lausanne, 1997,200 p. E. Hisdal, Logical structures for representation of knowledge and uncertainty, Physica-Verlag, Heidelberg, Germany, 199B. M. Jamshidi, A. Titli, L.A. Zadeh, S. Boverie, Applications of Fuzzy LogiC Towards High Machine Intelligence Quotient Systems, Prentice-Hall, Englewood Cliffs, NJ, 1997,423 p. F. Lootsma, Fuzzy Logic for Planning and Decision Making Applied Optimization 8. Kluwer Academic Publ., Dordrecht, 1998. W. Mielczarski, Fuzzy Logic Techniques in Power Systems, Physica-Verlag, Berlin, 1998, 456 p. J.N. Mordeson, P.S. Nair, Fuzzy Mathematics An Introduction for Engineers and Scientists. Physica-Verlag, Berlin, 1998,270 p. W. Pedrycz, Computational Intelligence An Introduction, CRC Press, Boca Raton, FL, 1997,304 p. M. Reghis, E. Roventa, Classical and Fuzzy Concepts in Mathematical Logic and Applications, CRC Press, Boca Raton, FL, 1998. L. Reznik, Fuzzy Controllers, Newness Press, UK, 1997. Schindler, Fuzzy-Datenanalyse durch kontextbasierte Datenbankanfragen, Deutscher Universitiits Verlag, Leverkusen, Germany, 1998. O. Wolkenhauer, Possibility Theory with Applications to Data Analysis. Research Studies Press, Hertfordshire, UK, 1998, 290 p. Edited volumes

Fuzzy sets and systems

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A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

Mean-variance model for portfolio optimization problem in the simultaneous presence of random and uncertain returns

Uncertain random variables are used to describe the phenomenon of simultaneous appearance of both uncertainty and randomness in a complex system. For modeling multi-objective decision-making problems with uncertain random parameters, a class of uncertain random optimization is suggested for decision systems in this paper, called the uncertain random multi-objective programming. For solving the uncertain random programming, some notions of the Pareto solutions and the compromise solutions as well as two compromise models are defined. Subsequently, some properties of these models are investigated, and then two equivalent deterministic mathematical programming models under some particular conditions are presented. Some numerical examples are also given for illustration.

Multi-objective optimization in uncertain random environments

In practice, some special LR fuzzy numbers, like the triangular fuzzy number, the Gaussian fuzzy number and the Cauchy fuzzy number, are widely used in many areas to deal with various vague information. With regard to these special LR fuzzy numbers, called regular LR fuzzy numbers in this paper, an operational law is proposed for fuzzy arithmetic, providing a novel approach to analytically and exactly calculating the inverse credibility distribution of some specific arithmetical operations based on the credibility measure. As an application of the operational law, an equivalent form of the expected value operator as well as a theorem for computing the expected value of strictly monotone functions is suggested. Finally, we utilize the operational law to construct a solution framework of fuzzy programming with parameters of regular LR fuzzy numbers, and such type of fuzzy programming problems can be handled by the operational law as the classic deterministic programming without any particular solving techniques.

Fuzzy arithmetic on LR fuzzy numbers with applications to fuzzy programming

This paper investigates the uncertain minimum spanning tree (UMST) problem where the edge weights are assumed to be uncertain variables. In order to propose effective solving methods for the UMST problem, path optimality conditions as well as some equivalent definitions for two commonly used types of UMST, namely, uncertain expected minimum spanning tree (expected UMST) and uncertain α-minimum spanning tree (α-UMST), are discussed. It is shown that both the expected UMST problem and the α-UMST problem can be transformed into an equivalent classical minimum spanning tree problem on a corresponding deterministic graph, which leads to effective algorithms with low computational complexity. Furthermore, the notion of uncertain most minimum spanning tree (most UMST) is initiated for an uncertain graph, and then the equivalent relationship between the α-UMST and the most UMST is proved. Numerical examples are presented as well for illustration.

Path Optimality Conditions for Minimum Spanning Tree Problem with Uncertain Edge Weights

The quadratic minimum spanning tree problem is to find a spanning tree on a graph that minimizes a quadratic objective function of the edge weights. In this paper, the quadratic minimum spanning tree problem is concerned on the graph with edge weights being assumed as uncertain variables. The notion of the uncertain quadratic -minimum spanning tree is introduced by using the uncertain chance constraints. It is shown that the problem of finding an uncertain quadratic - minimum spanning tree can be handled in the framework of the deterministic quadratic minimum spanning tree problem requiring no particular solving methods.

Uncertain Quadratic Minimum Spanning Tree Problem

The inverse shortest path problem is to minimize the modification on the edge weights such that a predetermined path becomes the shortest one from the origin to the destination with respect to the new edge weights. In this paper, the inverse shortest path problem is considered on a graph with uncertain edge weights. It is shown that the model of the uncertain inverse shortest path problem can be transformed into a deterministic counterpart and then be solved efficiently. A numerical example is presented as well for illustration.

Ke Wang

Papers

Multi-objective optimization in uncertain random environments

Fuzzy arithmetic on LR fuzzy numbers with applications to fuzzy programming

Path Optimality Conditions for Minimum Spanning Tree Problem with Uncertain Edge Weights

Uncertain Quadratic Minimum Spanning Tree Problem

An Inverse Shortest Path Problem on an Uncertain Graph