Showing papers on "Fuzzy associative matrix published in 2005"

Fuzzy Mathematical Programming and Fuzzy Matrix Games

Abstract: Crisp matrix and bi-matrix games: some basic results.- Fuzzy sets.- Fuzzy numbers and fuzzy arithmetic.- Linear and quadratic programming under fuzzy environment.- Duality in linear and quadratic programming under fuzzy environment.- Matrix games with fuzzy goals.- Matrix games with fuzzy pay-offs.- More on matrix games with fuzzy pay-offs.- Fuzzy Bi-Matrix Games.- Modality and other approaches for fuzzy linear programming.

Stochastic stability analysis of fuzzy hopfield neural networks with time-varying delays

Abstract: The ordinary Takagi-Sugeno (TS) fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied. The model of SFVDHNN is first established as a modified TS fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays. Secondly, the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach. Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages.

Supervisory control of fuzzy discrete event systems: a formal approach

Abstract: Fuzzy discrete event systems (DESs) were proposed recently by Lin and Ying, which may better cope with the real-world problems of fuzziness, impreciseness, and subjectivity such as those in biomedicine. As a continuation of, in this paper, we further develop fuzzy DESs by dealing with supervisory control of fuzzy DESs. More specifically: 1) we reformulate the parallel composition of crisp DESs, and then define the parallel composition of fuzzy DESs that is equivalent to that in . Max-product and max-min automata for modeling fuzzy DESs are considered, 2) we deal with a number of fundamental problems regarding supervisory control of fuzzy DESs, particularly demonstrate controllability theorem and nonblocking controllability theorem of fuzzy DESs, and thus, present the conditions for the existence of supervisors in fuzzy DESs; 3) we analyze the complexity for presenting a uniform criterion to test the fuzzy controllability condition of fuzzy DESs modeled by max-product automata; in particular, we present in detail a general computing method for checking whether or not the fuzzy controllability condition holds, if max-min automata are used to model fuzzy DESs, and by means of this method we can search for all possible fuzzy states reachable from initial fuzzy state in max-min automata. Also, we introduce the fuzzy n-controllability condition for some practical problems, and 4) a number of examples serving to illustrate the applications of the derived results and methods are described; some basic properties related to supervisory control of fuzzy DESs are investigated. To conclude, some related issues are raised for further consideration.

An improved robust fuzzy-PID controller with optimal fuzzy reasoning

Abstract: Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy reasoning with a PID control structure will generate a new type of fuzzy-PID control schemes with inherent optimal-tuning features for both local optimal performance and global tracking robustness. This new fuzzy-PID controller is then analyzed quantitatively and compared with other existing fuzzy-PID control methods. Both analytical and numerical studies clearly show the improved robustness of the new fuzzy-PID controller.

Fuzzy Probabilities : New Approach and Applications

Abstract: Fuzzy Sets.- Fuzzy Probability Theory.- Discrete Fuzzy Random Variables.- Fuzzy Queuing Theory.- Fuzzy Markov Chains.- Fuzzy Decisions Under Risk.- Continuous Fuzzy Random Variables.- Fuzzy Inventory Control.- Joint Fuzzy Probability Distributions.- Applications of Joint Distributions.- Functions of a Fuzzy Random Variable.- Functions of Fuzzy Random Variables.- Law of Large Numbers.- Sums of Fuzzy Random Variables.- Conclusions and Future Research.

Stability analysis of fuzzy control systems subject to uncertain grades of membership

Abstract: This paper presents relaxed stability conditions for fuzzy control systems subject to parameter uncertainties. As the parameter uncertainties introduce uncertain grades of membership to the fuzzy control systems, the favorable property offered by sharing the same premises in the fuzzy plant models and fuzzy controllers cannot be employed to enhance the stabilization ability of the fuzzy control systems. To widen the applicability of the fuzzy control approach, fuzzy control systems subject to uncertain grades of membership will be investigated. New relaxed stability conditions will be derived to guarantee the stability of this class of fuzzy control systems. A numerical example will be given to show the effectiveness of the proposed approach.

Matrix games with fuzzy goals and fuzzy payoffs

Abstract: A two person zero sum matrix game with fuzzy goals and fuzzy payoffs is considered and its solution is conceptualized using a suitable defuzzification function. Also, it is proved that such a game is equivalent to a primal–dual pair of certain fuzzy linear programming problems in which both goals as well as parameters are fuzzy.

Fuzzy rule interpolation for multidimensional input spaces with applications: a case study

Abstract: Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications.

Fuzzy relation analysis in fuzzy time series model

Abstract: Fuzzy relation is a crucial connector in presenting fuzzy time series model. However, how to obtain a fuzzy relation matrix to represent a time-invariant relation is still a question. Based on the concept of fuzziness in Information Theory, the concept of entropy is applied to measure the degrees of fuzziness when a time-invariant relation matrix is derived. Finally, an example is illustrated to show that the proposed method could obtain more accurate and robust results in forecasting.

Simulating Continuous Fuzzy Systems

Abstract: In previous studies we concentrated on utilizing crisp, numeric simulation to produce discrete event fuzzy systems simulations. Then we extended this research to the simulation of continuous fuzzy systems models. In this study, we continue our study of continuous fuzzy systems using crisp continuous simulation. Consider a crisp continuous system whose process of evolution depends on differential equations. Such a system contains a number of parameters that must be estimated. Usually point estimates are computed and used in the model. However, these point estimates typically have uncertainty associated with them. We propose to incorporate uncertainty by using fuzzy numbers as estimates of these unknown parameters. Fuzzy parameters convert the crisp system into a fuzzy system. Trajectories describing the behavior of the system become fuzzy curves. We will employ crisp continuous simulation to estimate these fuzzy trajectories. Three examples are discussed.

On logic-based intelligent systems

Abstract: First, a new matrix product, called the semitensor product of matrices, is introduced. Then the logic operators are expressed in matrix form. Based on this form, the fuzzy logic is deduced in an axiomatic form. Finally, the logic-based intelligent control is considered.

A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques

Abstract: In this paper, we extend the work of Kraft et al. to present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. First, we present a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster centers of document clusters. Then, we present a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user's query for query expansion and to guide the information retrieval system to retrieve documents relevant to the user's request. The fuzzy logic rules can represent three kinds of fuzzy relationships (i.e., fuzzy positive association relationship, fuzzy specialization relationship and fuzzy generalization relationship) between index terms. The proposed fuzzy information retrieval method is more flexible and more intelligent than the existing methods due to the fact that it can expand users' queries for fuzzy information retrieval in a more effective manner.

A class of hierarchical fuzzy systems with constraints on the fuzzy rules

Abstract: This paper presents a class of hierarchical fuzzy systems where previous layer outputs are used not in IF-parts, but only in THEN-parts of the fuzzy rules of the current layer. The proposed scheme is shown to be a universal approximator to any real continuous function on a compact set if complete fuzzy sets are used in the IF-parts of the fuzzy rules with singleton fuzzifier and center average defuzzifier. From the example of ball-and-beam control system simulation, it is demonstrated that the proposed scheme approximates with high accuracy a model nonlinear controller with fewer fuzzy rules than a centralized fuzzy system, and its control performance is comparable to that of a nonlinear controller.

Fuzzy logic derivation of neural network models with time delays in subsystems

Abstract: This paper extends the Takagi-Sugeno (T-S) fuzzy model representation to analyze the stability of interconnected systems in which there exist time delays in subsystems. A novel stability criterion which can be solved numerically is presented in terms of Lyapunov's theory for fuzzy interconnected models. In this paper, we use linear difference inclusion (LDI) state-space representation to represent the fuzzy model. Then, the linear matrix inequality (LMI) optimization algorithm is employed to find common solution and then guarantee the asymptotic stability.

Robust fuzzy control for a plant with fuzzy linear model

Abstract: A robust complexity reduced proportional-integral-derivative (PID)-like fuzzy controllers is designed for a plant with fuzzy linear model. The plant model is described with the expert's linguistic information involved. The linguistic information for the plant model is represented as fuzzy sets. In order to design a robust fuzzy controller for a plant model with fuzzy sets, an approach is developed to implement the best crisp approximation of fuzzy sets into intervals. Then, Kharitonov's Theorem is applied to construct a robust fuzzy controller for the fuzzy uncertain plant with interval model. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controller is significantly reduced. The parameters in the robust fuzzy controller are determined to satisfy the stability conditions. The robustness of the designed fuzzy controller is discussed. Also, with the provided definition of relative robustness, the robustness of the complexity reduced fuzzy controller is compared to the classical PID controller for a second-order plant with fuzzy linear model. The simulation results are included to show the effectiveness of the designed PID-like robust fuzzy controller with the complexity reduced fuzzy mechanism.

Fuzzy versus quantitative association rules: a fair data-driven comparison

Abstract: As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.

On fuzzy preference relation in group decision making

Abstract: In this article, we propose a new property called ‘comparable’ for fuzzy preference relation. We show that if a fuzzy preference relation satisfies reciprocal, transitive and comparable, it needs only O(n) comparisons of fuzzy numbers to rank n fuzzy numbers with fuzzy preference relation, which is more competitive than traditional methods that needs to calculate (n(n − 1))/2 preference relations.

Flexible Takagi-Sugeno fuzzy systems

Abstract: In the paper, a new class of Takagi-Sugeno fuzzy systems is derived. Various parameters and weights are incorporated into construction of such systems. The approach presented in the paper introduces more flexibility to the structure and design of neuro-fuzzy systems.

Fuzzy description of topological relations I: a unified fuzzy 9-intersection model

Abstract: First, the impacts of uncertainty of position and attribute on topological relations and the disadvantages of qualitative methods in processing the uncertainty of topological relations are concluded. Second, based on the above point, the fuzzy membership functions for dividing topology space of spatial object and for describing uncertainty of topological relations are proposed. Finally, the fuzzy interior, exterior and boundary are defined according to those fuzzy membership functions, and then a fuzzy 9-intersection model that can describe the uncertainty is constructed. Since fuzzy 9-intersection model is based on fuzzy set, not two-value logic, the fuzzy 9-intersection model can describe the impacts of position and attribute of spatial data on topological relations, and the uncertainty of topological relations between fuzzy objects, relations between crisp objects and fuzzy objects, and relations between crisp objects in a united model.

Fuzzy Qualitative Trigonometry

Abstract: This paper proposes fuzzy qualitative representation of trigonometry (FQT) in order to bridge the gap between qualitative and quantitative representation of physical systems using trigonometry Fuzzy qualitative coordinates are defined by replacing a unit circle with a fuzzy qualitative circle; the Cartesian translation and orientation are replaced by their fuzzy membership functions Trigonometric functions, rules and the extensions to triangles in Euclidean space are converted into their counterparts in fuzzy qualitative coordinates using fuzzy logic and qualitative reasoning techniques We developed a MATLAB toolbox XTrig in terms of 4-tuple fuzzy numbers to demonstrate the characteristics of the FQT This approach addresses a representation transformation interface to connect qualitative and quantitative descriptions of trigonometry-related systems (eg, robotic systems)