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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Korean Institute of Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 2, Issue 3 - Sep 1992
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An Extraction of Linguistic Fuzzy Model from Input/Output Relation
Journal of Korean Institute of Intelligent Systems, volume 2, issue 3, 1992, Pages 3~16
Structures of Fuzzy Relations
Min, K.C ;
Journal of Korean Institute of Intelligent Systems, volume 2, issue 3, 1992, Pages 17~21
In this paper we consider the notion of fuzzy relation as a generalization of that of fuzzy set. For a complete Heyting algebra L. the category set(L) of all L-fuzzy sets is shown to be a bireflective subcategory of the category Rel(L) of all L-fuzzy relations and L-fuzzy relation preserving maps. We investigate categorical structures of subcategories of Rel(L) in view of quasitopos. Among those categories, we include the category L-fuzzy similarity relations with respect to both max-min and max-product compositions, respectively, as a cartesian closed topological category. Moreover, we describe exponential objects explicitly in terms of function space.
Development of Fuzzy Objective Functioin for Establishing the Region Correspondence
Soh, Young-sung ;
Journal of Korean Institute of Intelligent Systems, volume 2, issue 3, 1992, Pages 22~28
One of the challenging problems in dynamic scene analysis is the correspondence problem. Points and lines have been two major entities for establishing the correspondence among suxcessive frmes and gave rise to discrete approach to dynamic scene analysis. SOme researchers take continuous approach to analyse the motion. There it is usually assumed that some sort of region correspondence has already been established. In this paper, we propose a method based on fuzzy membership function for solving region correspondence problem.
A Systematic Design of Automatic Fuzzy Rule Generation for Dynamic System
Kang, Hoon ; Kim, Young-Ho ; Jeon, Hong-Tae ;
Journal of Korean Institute of Intelligent Systems, volume 2, issue 3, 1992, Pages 29~39
We investigate a systematic design procedure of automatic rule generation of fuzzy logic based controllers for highly nonlinear dynamic systems such as an engine dynamic modle. By "automatic rule generation" we mean autonomous clustering or collection of such meaningful transitional relations from one conditional subspace to another. During the design procedure, we also consider optimaly control strategies such as minimum squared error, near minimum time, minimum energy or combined performance critiera. Fuzzy feedback control systems designed by our method have the properties of closed-loop stability, robustness under parameter variabitions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller dwsign to a highly nonlinear model of engine idling speed control.d control.
Automatic Fuzzy Rule Generation Utilizing Genetic Algorithms
Hee, Soo-Hwang ; Kwang, Bang-Woo ;
Journal of Korean Institute of Intelligent Systems, volume 2, issue 3, 1992, Pages 40~49
In this paper, an approach to identify fuzzy rules is proposed. The decision of the optimal number of fuzzy rule is made by means of fuzzy c-means clustering. The identification of the parameters of fuzzy implications is carried out by use of genetic algorithms. For the efficinet and fast parameter identification, the reduction thechnique of search areas of genetica algorithms is proposed. The feasibility of the proposed approach is evaluated through the identification of the fuzzy model to describe an input-output relation of Gas Furnace. Despite the simplicity of the propsed apprach the accuracy of the identified fuzzy model of gas furnace is superior as compared with that of other fuzzy modles.
A Design of the Fuzzy Neural Network Image Recognizer
Kim, Dae-Su ;
Journal of Korean Institute of Intelligent Systems, volume 2, issue 3, 1992, Pages 50~57
Neural networks have become more popular recently and are now being applied to numerous fiedls. One of the major applications of neural networks is image recognition. Various image recognition system have been proposed so far, but there is no definite solution yet. In this paper, we propose a design of Fuzzy Neural Network Image Recognizer(FNNIR). Our model uses a fuzzy neural network model, named SONN[KIM90]. This model returns the information of the number of clusters and cluster and cluster center values for a given image data ste. Unlike the well-kinwn backpropagation technique, we do not need retraining for new data. Our newly designed image recongitionsystem FNNIR that uses fuzzy merger is proposed and experimented for a sample color image.
Application of Genetic Algorithm to Hybrid Fuzzy Inference Engine
Park, Sae-hie ; Chung, Sun-tae ; Jeon, Hong-tae ;
Journal of Korean Institute of Intelligent Systems, volume 2, issue 3, 1992, Pages 58~67
This paper presents a method on applying Genetric Algorithms(GA), which is a well-know high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilized Sugeno's hybrid inference method. which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the iptimal parameters in the FLC. The proposed approach will be demonstrated using 2 d. o. f robot manipulator to verify its effectiveness.