Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
International Journal of Fuzzy Logic and Intelligent Systems
Journal Basic Information
Journal DOI :
Korean Institute of Intelligent Systems
Editor in Chief :
Volume & Issues
Volume 4, Issue 3 - Dec 2004
Volume 4, Issue 2 - Sep 2004
Volume 4, Issue 1 - Jun 2004
Selecting the target year
A Balanced Model Reduction for Fuzzy Systems with Time Varying Delay
Yoo, Seog-Hwan ; Park, Byung-Jae ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 1~6
DOI : 10.5391/IJFIS.2004.4.1.001
This paper deals with a balanced model reduction for T-S(Takagi-Sugeno) fuzzy systems with time varying state delay. We define a generalized controllability gramian and a generalized observability gramian for a stable T-S fuzzy delayed systems. We obtain a balanced state space realization using the generalized controllability and observability gramian and obtain a reduced model by truncating states from the balanced state space realization. We also present an upper bound of the approximation error. The generalized controllability gramian and observability gramian can be computed from solutions of linear matrix inequalities. We demonstrate the efficacy of the suggested method by illustrating a numerical example.
A Biologically Inspired New Hardware Fault Detection: immunotronic and Genetic Algorithm-Based Approach
Lee, Sanghyung ; Kim, Euntai ; Park, Mignon ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 7~11
DOI : 10.5391/IJFIS.2004.4.1.007
This paper proposes a new immunotronic approach for the fault detection in hardware. The suggested method is, inspired by biology and its implementation is based on genetic algorithm. Tolerance conditions in the immunotronic system for fault detection correspond to the antibodies in the biological immune system. A novel algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity and GA optimization is employed to select mature tolerance conditions in immunotronic fault detection system. The suggested method is applied to the fault detection for MCNC benchmark FSMs (finite state machines) and its effectiveness is demonstrated by the computer simulation.
A Study on the Introduction of Fuzzy system into the Decision-Making process of HVAC designers
Woo, Se-Jin ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 12~17
DOI : 10.5391/IJFIS.2004.4.1.012
This study is designed to grope for logical methods in the decision-making process of human beings such as creation and analysis. With this in mind, the paper worked with a process where the designers of a design team gather and analyze their opinions in a design process to decide on the HVAC system of buildings. The paper introduced the fuzzy theory, or one of the methods to quantitatively describe language values with ambiguous features, suggesting a method to determine the judgement and suggestion values of the HVAC designers with the characteristics of language variables as the values of design factors greatly influencing the HVAC system. As a result, the paper tested the possibility of the fuzzy system as a logical method to gather the judgement of HVAC designers in a stage of HVAC type selection exerting a great influence on the experience and judgement of the designers and having powerful linguistic features and to determine an appropriate HVAC type which can satisfy the suggested values of related design factors.
An Emotion Classification Based on Fuzzy Inference and Color Psychology
Son, Chang-Sik ; Chung, Hwan-Mook ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 18~22
DOI : 10.5391/IJFIS.2004.4.1.018
It is difficult to understand a person's emotion, since it is subjective and vague. Therefore, we are proposing a method by which will effectively classify human emotions into two types (that is, single emotion and composition emotion). To verify validity of te proposed method, we conducted two experiments based on general inference and
-cut, and compared the experimental results. In the first experiment emotions were classified according to fuzzy inference. On the other hand in the second experiment emotions were classified according to
-cut. Our experimental results showed that the classification of emotion based on a- cut was more definite that that based on fuzzy inference.
Control of the Mobile Robot Navigation Using a New Time Sensor Fusion
Tack, Han-Ho ; Kim, Chang-Geun ; Kim, Myeong-Kyu ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 23~28
DOI : 10.5391/IJFIS.2004.4.1.023
This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations. Finally, the new space and time sensor fusion(STSF) scheme is applied to the control of a mobile robot in an unstructured environment as well as structured environment.
Digital Library System by Advanced Distributed Agent Platform
Cho, Young-Im ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 29~33
DOI : 10.5391/IJFIS.2004.4.1.029
I propose a personalized digital library system (PDLS) based on an advanced distributed agent platform. The new platform is developed by improving the DECAF (Distributed Environment-Centered Agent Framework) which is one of the conventional distributed agent development toolkits. Also, a mobile ORB (Object Request Broker), Voyager, and a new multi agent negotiation algorithm are adopted to develop the advanced platform. The new platform is for mobile multi agents as well as the distributed environment, whereas the DECAF is for the distributed and non-mobile environment. From the results of the simulation the searched time of PDLS is lower, as the numbers of servers and agents are increased. And the user satisfaction is four times greater than the conventional client-server model. Therefore, the new platform has some optimality and higher performance in the distributed mobile environment.
Efficient Multi-way Tree Search Algorithm for Huffman Decoder
Cha, Hyungtai ; Woo, Kwanghee ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 34~39
DOI : 10.5391/IJFIS.2004.4.1.034
Huffman coding which has been used in many data compression algorithms is a popular data compression technique used to reduce statistical redundancy of a signal. It has been proposed that the Huffman algorithm can decode efficiently using characteristics of the Huffman tables and patterns of the Huffman codeword. We propose a new Huffman decoding algorithm which used a multi way tree search and present an efficient hardware implementation method. This algorithm has a small logic area and memory space and is optimized for high speed decoding. The proposed Huffman decoding algorithm can be applied for many multimedia systems such as MPEG audio decoder.
Existence and Uniqueness of Fuzzy Solutions for the nonlinear Fuzzy Integro-Differential Equation on E
Kwun, Young-Chel ; Han, Chang-Woo ; Kim, Seon-Yu ; Park, Jong-Seo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 40~44
DOI : 10.5391/IJFIS.2004.4.1.040
In this paper we study the existence and uniqueness of fuzzy solutions for the nonlinear fuzzy integro-differential equations on
by using the concept of fuzzy number of dimension n whose values are normal, convex, upper semicontinuous and compactly supported surface in
be the set of all fuzzy numbers in
with edges having bases parallel to axis
Fuzzy Power Factor Control Systems
Cho, Seong-Won ; Kim, Jae-Min ; Jung, Jae-Yoon ; Lim, Cheol-Su ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 45~49
DOI : 10.5391/IJFIS.2004.4.1.045
A method for obtaining the power energy with high quality is to keep the power factor for a load as close to unity as feasible. In this paper, we present a new method to improve the power factor for a load. The proposed method uses fuzzy control techniques in order to determine how many parallel capacitors are to be connected to the load for the correction of the power factor.
High-speed Fuzzy Inference System in Integrated GUI Environment
Lee, Sang-Gu ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 50~55
DOI : 10.5391/IJFIS.2004.4.1.050
We propose an intgrated Gill environment system having only integer fuzzy operations in the consequent part and the defuzzification stage. In this paper, we also propose an integrated Gill environment system with 4 parallel fuzzy processing units to be operated in parallel on the classification of the sensed image data. In this, we solve the problems of taking longer times as the fuzzy real computations of [0, 1] by using the integer pixel conversion algorithm to convert lines of each fuzzy linguistic term to the closest integer pixels. This procedure is performed automatically in the GUI application program. As a Gill environment, PCI transmission, image data pre-processing, integer pixel mapping and fuzzy membership tuning are considered. This system can be operated in parallel manner for MIMO or MISO systems.
Implementation of Process System and Intelligent Monitoring Environment using Neural Network
Kim, Young-Tak ; Kim, Gwan-Hyung ; Kim, Soo-Jung ; Lee, Sang-Bae ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 56~62
DOI : 10.5391/IJFIS.2004.4.1.056
This research attempts to suggest a detecting method for cutting position of an object using the neural network, which is one of intellectual methods, and the digital image processing method. The extraction method of object information using the image data obtained from the CCD camera as a replacement of traditional analog sensor thanks to the development of digital image processing. Accordingly, this research determines the threshold value in binary-coding of an input image with the help of image processing method and the neural network for the real-time gray-leveled input image in substitution for lighting; as a result, a specific position is detected from the processed binary-coded image and an actual system designed is suggested as an example.
Improvement of Control Performance by Data Fusion of Sensors
Na, Seung-You ; Shin, Dae-Jung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 63~69
DOI : 10.5391/IJFIS.2004.4.1.063
In this paper, we propose a general framework for sensor data fusion applied to control systems. Since many kinds of disturbances are introduced to a control system, it is necessary to rely on multisensor data fusion to improve control performance in spite of the disturbances. Multisensor data fusion for a control system is considered a sequence of making decisions for a combination of sensor data to make a proper control input in uncertain conditions of disturbance effects on sensors. The proposed method is applied to a typical control system of a flexible link system in which reduction of oscillation is obtained using a photo sensor at the tip of the link. But the control performance depends heavily on the environmental light conditions. To overcome the light disturbance difficulties, an accelerometer is used in addition to the existing photo sensor. Improvement of control performance is possible by utilizing multisensor data fusion for various output responses to show the feasibility of the proposed method in this paper.
Intelligent Control of Nonlinear dynamic system Using Immune Fuzzy Fusion
Kim, Dong-Hwa ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 70~78
DOI : 10.5391/IJFIS.2004.4.1.070
This paper proposes non-linear control method using immune algorithm based fuzzy logic. Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because gain of the PID controller has to be manually tuned by trial and error. An inverted pendulum control problem is selected to illustrate the efficiency of the proposed method and defines relationship state variables
using immune fuzzy.
Low Power Design of the Neuroprocessor
Pandya, A.S. ; Agarwal, Ankur ; Chae, G.Y. ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 79~83
DOI : 10.5391/IJFIS.2004.4.1.079
This paper presents the performance analysis for CPL based design of a Low power digital neuroprocessor. We have verified the functionality of the components at the high level using Verilog and carried out the simulations in Silos. The components of the proposed digital neuroprocessor have also been verified at the layout level in LASI. The layouts have then been simulated and analyzed in Winspice for their timing characteristics. The result shows that the proposed digital neuroprocessor consistently consumes less power than other designs of the same function. It can also be seen that the proposed functions have lesser propagation delay and thus higher speed compared to the other designs.
Machine Cell Formation using A Classification Neural Network
Lee, Kyung-Mi ; Lee, Keon-Myung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 84~89
DOI : 10.5391/IJFIS.2004.4.1.084
The machine cell formation problem is the problem to group machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts, and inter-cell part movements in cellular manufacturing systems and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net which is a kind of neural network model. To show the applicability of the proposed method, it presents some experiment results and compares the method with other cell formation methods. From the experiments, we observed that the proposed method could produce good cells for the machine cell formation problem.
Modelling of intelligent training system by a generalized net
Lee, Chae-Jang ; Kim, Tae-Kyun ; Park, Dal-Won ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 90~96
DOI : 10.5391/IJFIS.2004.4.1.090
A generalized net model of a training process with modified fuzzy marks in answers of the trained objects and their current and total modified fuzzy evaluation, tharning themes, estimation criteria and others are discussed and interpreted.
Multivariate Analysis of Variance for Fuzzy Data
Kang, Man-Ki ; Han, Sung-Il ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 97~100
DOI : 10.5391/IJFIS.2004.4.1.097
We propose some properties of fuzzy multivariate analysis of variance by fuzzy vector operation with agreement index. We deals fuzzy null hypotheses and fuzzy alternative hypothesis and define the agreement index for the grades of the judgements that the hypothesis is rejection or acceptance. Finally, we provide an example to evaluate the judgements.
On Motion Planning for Human-Following of Mobile Robot in a Predictable Intelligent Space
Jin, Tae-Seok ; Hashimoto, Hideki ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 101~110
DOI : 10.5391/IJFIS.2004.4.1.101
The robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, humans and robots need to be in close proximity to each other as much as possible. Moreover, it is necessary for their interactions to occur naturally. It is desirable for a robot to carry out human following, as one of the human-affinitive movements. The human-following robot requires several techniques: the recognition of the moving objects, the feature extraction and visual tracking, and the trajectory generation for following a human stably. In this research, a predictable intelligent space is used in order to achieve these goals. An intelligent space is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to follow a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to follow the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and following of the walking human with the mobile robot are presented.
Path Tracking Control Using a Wavelet Based Fuzzy Neural Network for Mobile Robots
Oh, Joon-Seop ; Park, Yoon-Ho ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 111~118
DOI : 10.5391/IJFIS.2004.4.1.111
In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.
Sparse Data Cleaning using Multiple Imputations
Jun, Sung-Hae ; Lee, Seung-Joo ; Oh, Kyung-Whan ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 4, issue 1, 2004, Pages 119~124
DOI : 10.5391/IJFIS.2004.4.1.119
Real data as web log file tend to be incomplete. But we have to find useful knowledge from these for optimal decision. In web log data, many useful things which are hyperlink information and web usages of connected users may be found. The size of web data is too huge to use for effective knowledge discovery. To make matters worse, they are very sparse. We overcome this sparse problem using Markov Chain Monte Carlo method as multiple imputations. This missing value imputation changes spare web data to complete. Our study may be a useful tool for discovering knowledge from data set with sparseness. The more sparseness of data in increased, the better performance of MCMC imputation is good. We verified our work by experiments using UCI machine learning repository data.