In fuzzy modeling, it is relatively easy to manually define rough fuzzy rules for a target system by intuition. It is, however, time-consuming and difficult to fine-tune them to improve their behavior. This paper describes a tuning method for fuzzy models which is applicable regardless of the form of fuzzy rules and the used defuzzification method. For this purpose, this paper proposes a fuzzy neural network model which can embody fuzzy models. The proposed model provides the functions to perform fuzzy inference and to tune the parameters for the shape of antecedent linguistic terms, the relative importance degrees of rules, and the relative importance degrees of antecedent linguistic terms in rules. In addition, to show its applicability, we perform some experiments and present the results.
These papers are originally published in the proceedings of Korea fuzzy logic and intelligent systems society (KFIS) fall conference in 1999. Eight papers are selected for this special issue. Major topics of them are fuzzy theory, neural network, inference system, intelligent controller, etc. In this issue, Seihwan Park and Hyung Lee-Kwang extend the concept of fuzzy hypergraph to type-2 fuzzy hypergraph using type-2 fuzzy sets. It has not only the same properties of hypergraphs but also the extended properties of them. It is also shown that interval valued fuzzy hypergraph is a special case of type-2 fuzzy hypergraph. Jung-Heum Yon, Yong-Taek Kim, Jae-Yong Seo and Hong-Tae Jeon design an efficient neural network called dynamic multidimensional wavelet neural network. It can perform an effective dynamic mapping with less dimensions of the input signal. These features show one way to compensate the weakness of the diagonal recurrent neural network and feedforward wavelet neural network. Yigon Kim, Yang Hee Jung and Young Chel Bae propose a new method for diagnosis of insulation aging using wavelet. It measures the partial discharge on-line from data acquisition system and analyses it using wavelet to acquire 21) patterns. They design a neuro-fuzzy model that diagnoses an electrical equipment using the data. Byung-Jae Choi, Seong-Woo Kwak and Byung Kook Kim develop an adaptive fuzzy logic controller. A sole input fuzzy variable is used to simplify the design procedure and the switching hyperplane of sliding mode control is used to improve the adaptability. Myung-Geun Chun, Keun-Chang Kwak and Jeong-Woong Ryu show an efficient fuzzy rule generation scheme for adaptive network-based fuzzy inference system using the conditional fuzzy c-means and fuzzy equalization methods. They apply this method to the truck backer-upper control and Box-Jenkins modeling problem. Daijin Kim proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Twostage classification method is used. All data are classified by using the lower approximation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. Min-Soeng Kim, Sun-Gi Hong and Ju-Jang Lee incorporate the Q-learning algorithm into the fuzzy logic controller. Modified fuzzy rule is used for the incorporation. As a result, a fuzzy logic controller is obtained that can learn through experience. Dong Hwa Kim designs a new 2-DOF PID controller and applies it to the operating data based transfer function of Gun-san Gas turbine in Korea. We hope that this issue can be helpful to readers and we appreciate professor Kaoru Hirota for his interest and support for the publication.
This paper presents a fuzzy traffic controller for a set of intersections and its simulation results. In the developed system, each intersection has its own traffic controller. The controller of a intersection manages the phase sequence and the phase length dynamically according to its own and the neighboring traffic situations. To do this, we adopt a competitive scheme. All possible phases except the green phase compete to get the green signal. The controller consists of three modules: the next phase selection module, the green phase observing module, and the decision module. To carry out the performance evaluation of the controller, a simulator for the intersection group has been developed. The developed fuzzy traffic controller is compared with the vehicle-adjusted methods. The controller shows good performance in case where traffic patterns change over time and in heavy traffic situations.
Jee-Hyong LEE, Keon-Myung LEE, KyoungA SEONG, ChangBum KIM, and Hyung LEE-KWANG Dept. of Computer Science, KAIST ( Korea Advanced Institute of Science and Technology ), Yousung, Taejon, 305-701, Seoul Korea Abstract. This paper presents a tra c fuzzy controller for a set of intersections and its simulation results. To control a set of intersection we distribute controls to the controller at each intersection. The controller at an intersection manages phase sequences and phase lengths adaptively to its neighborhood's as well as its own tra c conditions. The simulation shows good performance in the case of time-varying tra c patterns and heavy tra c conditions.
If a point p is visible from m distinct points, the point p is said to be m-visible. For a simple polygonal shape art gallery P, it is shown that the minimum number of guards required for P to be m-visible is either 2(m−1)+1 or 3(m−1)+1 if P is a star-shaped polygon with the property that P is visible from only a boundary point. For any simple polygon with n vertices, it is also shown that the [n/3] × m guards are occasionally necessary and always sufficient. The algorithms to locate the determined guards on the given gallery (or polygon) are also presented.
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