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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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International Journal of Fuzzy Logic and Intelligent Systems
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Journal DOI :
Korean Institute of Intelligent Systems
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Volume & Issues
Volume 6, Issue 4 - Dec 2006
Volume 6, Issue 3 - Sep 2006
Volume 6, Issue 2 - Jun 2006
Volume 6, Issue 1 - Mar 2006
Selecting the target year
A Reinforcement Learning with CMAC
Kwon, Sung-Gyu ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 271~276
DOI : 10.5391/IJFIS.2006.6.4.271
To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC (Cerebellar Model Articulation Controller) is integrated into ASE controller. ASE-reinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training for CMAC is utilized to establish the look-up table and to produce discrete control outputs. In computer simulation, an ASE controller and a couple of ASE-CMAC neural network are trained to balance the inverted pendulum on a cart. The number of trials until the controllers are established and the learning performance of the controllers are evaluated to find that generalization ability of the CMAC improves the speed of the ASE-reinforcement learning enough to realize the cartpole control system.
Analysis of Client Propensity in Cyber Counseling Using Bayesian Variable Selection
Pi, Su-Young ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 277~281
DOI : 10.5391/IJFIS.2006.6.4.277
Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.
Development of Interactive Feature Selection Algorithm(IFS) for Emotion Recognition
Yang, Hyun-Chang ; Kim, Ho-Duck ; Park, Chang-Hyun ; Sim, Kwee-Bo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 282~287
DOI : 10.5391/IJFIS.2006.6.4.282
This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.
Existence and Uniqueness of Solutions for the Semilinear Fuzzy Integrodifferential Equations with Nonlocal Conditions and Forcing Term with Memory
Kwun, Young-Chel ; Park, Jong-Seo ; Kim, Seon-Yu ; Park, Jin-Han ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 288~292
DOI : 10.5391/IJFIS.2006.6.4.288
Many authors have studied several concepts of fuzzy systems. Balasubramaniam and Muralisankar (2004) proved the existence and uniqueness of fuzzy solutions for the semilinear fuzzy integrodifferential equation with nonlocal initial condition. Recently, Park, Park and Kwun (2006) find the sufficient condition of nonlocal controllability for the semilinear fuzzy integrodifferential equation with nonlocal initial condition. In this paper, we study the existence and uniqueness of solutions for the semilinear fuzzy integrodifferential equations with nonlocal condition and forcing term with memory in
by using the concept of fuzzy number whose values are normal, convex, upper semicontinuous and compactly supported interval in
Fuzzy Neural Network Based Sensor Fusion and It's Application to Mobile Robot in Intelligent Robotic Space
Jin, Tae-Seok ; Lee, Min-Jung ; Hashimoto, Hideki ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 293~298
DOI : 10.5391/IJFIS.2006.6.4.293
In this paper, a sensor fusion based robot navigation method for the autonomous control of a miniature human interaction robot is presented. The method of navigation blends the optimality of the Fuzzy Neural Network(FNN) based control algorithm with the capabilities in expressing knowledge and learning of the networked Intelligent Robotic Space(IRS). States of robot and IR space, for examples, the distance between the mobile robot and obstacles and the velocity of mobile robot, are used as the inputs of fuzzy logic controller. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a sensor fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process. Preliminary experiment and results are shown to demonstrate the merit of the introduced navigation control algorithm.
Fuzzy strongly (r, s) -semiopen sets
Lee, Seung-On ; Lee, Eun-Pyo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 299~303
DOI : 10.5391/IJFIS.2006.6.4.299
In this paper, we introduce the concepts of fuzzy strongly (r, s)-semiopen sets and fuzzy strongly (r, s)-semicontinuous mappings on the intuitionistic fuzzy topological space in Sostak's sense and then we investigate some of their characteristic properties.
GripLaunch: a Novel Sensor-Based Mobile User Interface with Touch Sensing Housing
Chang, Wook ; Park, Joon-Ah ; Lee, Hyun-Jeong ; Cho, Joon-Kee ; Soh, Byung-Seok ; Shim, Jung-Hyun ; Yang, Gyung-Hye ; Cho, Sung-Jung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 304~313
DOI : 10.5391/IJFIS.2006.6.4.304
This paper describes a novel way of applying capacitive sensing technology to a mobile user interface. The key idea is to use grip-pattern, which is naturally produced when a user tries to use the mobile device, as a clue to determine an application to be launched. To this end, a capacitive touch sensing system is carefully designed and installed underneath the housing of the mobile device to capture the information of the user's grip-pattern. The captured data is then recognized by dedicated recognition algorithms. The feasibility of the proposed user interface system is thoroughly evaluated with various recognition tests.
Hierarchical Behavior Control of Mobile Robot Based on Space & Time Sensor Fusion(STSF)
Han, Ho-Tack ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 314~320
DOI : 10.5391/IJFIS.2006.6.4.314
Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.
INTUITIONISTIC FUZZY WEAK CONGRUENCES ON A SEMIRING
Hur, Kul ; Jang, Su-Youn ; Lee, Keon-Chang ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 321~330
DOI : 10.5391/IJFIS.2006.6.4.321
We introduce the concept of intuitionistic fuzzy weak congruence on a semiring and obtain the relation between intuitionistic fuzzy weak congruence and intuitionistic fuzzy ideal of a semiring. Also we define and investigate intuitionistic fuzzy quotient semiring of a semiring over an intuitionistic fuzzy ideal or over an intuitionistic fuzzy weak congruence.
Multiclass SVM Model with Order Information
Ahn, Hyun-Chul ; Kim, Kyoung-Jae ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 6, issue 4, 2006, Pages 331~334
DOI : 10.5391/IJFIS.2006.6.4.331
Original Support Vsctor Machines (SVMs) by Vapnik were used for binary classification problems. Some researchers have tried to extend original SVM to multiclass classification. However, their studies have only focused on classifying samples into nominal categories. This study proposes a novel multiclass SVM model in order to handle ordinal multiple classes. Our suggested model may use less classifiers but predict more accurately because it utilizes additional hidden information, the order of the classes. To validate our model, we apply it to the real-world bond rating case. In this study, we compare the results of the model to those of statistical and typical machine learning techniques, and another multi class SVM algorithm. The result shows that proposed model may improve classification performance in comparison to other typical multiclass classification algorithms.