Hyung Lee-Kwang

Similarity measure between fuzzy sets and between elements

Abstract Two similarity measures are proposed: one for the similarity between fuzzy sets and the other between elements in fuzzy sets. With and example, it is shown that the proposed measures can be used in the behavior analysis in an organization.

A method for ranking fuzzy numbers and its application to decision-making

Since fuzzy numbers represent uncertain numeric values, it is difficult to rank them according to their magnitude. In the paper, a method for ranking fuzzy numbers is proposed. The method considers the overall possibility distributions of fuzzy numbers in their evaluations for ranking and provides users with a method of changing viewpoints for evaluations. Users represent their viewpoints with fuzzy sets. The method evaluates fuzzy numbers with a satisfaction function and the viewpoint given by users and then ranks the numbers according to their evaluation values. The satisfaction function is a measure of comparisons between fuzzy numbers. In order to illustrate the ranking method, two numeric examples are shown, and for the comparative study, our method is compared with four existing ranking methods through eight examples. As an example of potential applications, the proposed method is applied to a decision-making problem: a two-person game with fuzzy profit and loss. The ranking method is used to analyze player choices.

Generalized Petri Net Reduction Method

A reduction method of generalized Petri nets is proposed. This method is a generalization of the reduction method which was previously given by Lee-Kwang and Favrel. The proposed method is defined not on the basis of the dynamic behavior but of the structure of the net, and thus the test of reducible subnet can be done by a deterministic approach. The reduction preserves the properties such as liveness, boundedness, and proper termination, and allows easy analysis of generalized Petri nets.

Ranking fuzzy values with satisfaction function

Abstract This paper proposes a measure called the satisfaction function which estimates the satisfaction degree of arithmetic comparison relations (such as >,

Identification of l-fuzzy measure by genetic algorithms

Fuzzy measures is a measure for representing the membership degrees of an object to candidate sets. It is not easy to provide consistent fuzzy measure values with fuzzy measure properties since they have to be subjectively determined. Thus it induces an identification problem that determines measure values with fuzzy measure properties from human-provided measure values. The λ-fuzzy measure is a typical fuzzy measure widely used. Several methods have been developed for λ-fuzzy measure identification. Such methods, however, have restrictions on data set used in the identification, or require complicate computation, and thus not easy to use. Therefore, this paper proposes a λ-fuzzy measure identification method based on genetic algorithms, and shows its applicability by some experiments.

A fuzzy approach to elevator group control system

The elevator group control systems are the control systems that manage systematically, three or more elevators in order to efficiently transport the passengers. In the elevator group control system, the area-weight which determines the load biases of elevators is a control parameter closely related to the system performance. This correspondence proposes a fuzzy model based method to determine the area weight. The proposed method uses a two-stage fuzzy inference model which is built by the study of area-weight properties and expert knowledge. The proposed method shows more desirable results than the conventional method in simulations that use real traffic data. >

Design and implementation of FEGCS: fuzzy elevator group control system

The elevator group control systems (EGCS) are the control systems that manage systematically three or more elevators in order to efficiently transport the passengers. Most of the EGCSs have used the hall call assignment method to assign elevators in response to passengers calls. This paper proposes a control strategy generation method, a hall call assignment method based on fuzzy theory and the fuzzy elevator group control system (FEGCS). The control strategy of FEGCS is made using the classification of the passenger traffic and system managers requirements, and the hall calls are assigned to suitable elevators by the generated control strategy. The system is operated using the given control strategy which is defined by the system manager. The proposed system shows better results than the conventional methods in simulations, and the developed system is under the commercialization by an industrial company.

Fuzzy hypergraph and fuzzy partition

In this paper, the concept of hypergraph is extended to the fuzzy hypergraph. In the fuzzy hypergraph, the concepts of /spl alpha/-cut hypergraph, strength of edge and dual fuzzy hypergraph are developed. It is shown that the fuzzy hypergraph and /spl alpha/-cut hypergraph are useful to represent a fuzzy partition. An application example also shows that the strength of edge can be used to decompose the data set in a clustering problem. >

Tuning of fuzzy models by fuzzy neural networks

It is relatively easy to construct a rough fuzzy model with expert knowledge. It is difficult, however, to fine-tune the parameters of the fuzzy model in order to get improved behavior. For the purpose of tackling this problem, we propose a fuzzy neural network model. The proposed model utilizes a prior expert knowledge for target systems, and embodies fuzzy models which consist of fuzzy rules whose antecedent and consequent are fuzzy sets. The model is equipped with a fuzzy inferencing and tuning mechanism for model parameters by learning. It allows us to tune such parameters of fuzzy models as linguistic terms and relative rule importance. In addition, to show its applicability, we perform some experiments and present the results.

High speed flexible fuzzy hardware for fuzzy information processing

In this paper, we present a system called KAFA (Kaist Fuzzy Accelerator) which provides various fuzzy inference methods and fuzzy set operations. The basic idea of this study is to develop a more general purpose hardware system. The architecture has SIMD structure, which consists of two parts; a system control unit (main controller), and an arithmetic unit (fuzzy processing element (FPE)). Microinstruction codes are defined and any fuzzy operation can be programmed by using these microinstructions. Each FPE has the maximum speed of 10 M FLOPS. As the KAFA contains 128 FPEs, if a fuzzy set consists of 128 elements, we achieve the peak performance of 10 M FSOPS (fuzzy set operation per second) under 10 MHz clock frequency. This system also includes the parallel algorithms for defuzzification on the SIMD mode architecture using KAFA network. The prototype of the proposed architecture was developed with the FPGA chips. The speed of the KAFA holds promise for the development of the new fuzzy application system such as automatic control, fuzzy expert systems, real time systems and fuzzy databases.