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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 5, Issue 4 - Dec 2005
Volume 5, Issue 3 - Sep 2005
Volume 5, Issue 2 - Jun 2005
Volume 5, Issue 1 - Mar 2005
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A Co-Evolutionary Computing for Statistical Learning Theory
Jun Sung-Hae ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 281~285
DOI : 10.5391/IJFIS.2005.5.4.281
Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.
A simple method to compute a periodic solution of the Poisson equation with no boundary conditions
Moon Byung Doo ; Lee Jang Soo ; Lee Dong Young ; Kwon Kee-Choon ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 286~290
DOI : 10.5391/IJFIS.2005.5.4.286
We consider the poisson equation where the functions involved are periodic including the solution function. Let $R
A study on Generalized Synchronization in the State-Controlled Cellular Neural Network(SC-CNN)
Rae Youngchul ; Kim Yi-gon ; Tinduka Mathias ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 291~296
DOI : 10.5391/IJFIS.2005.5.4.291
In this paper, we introduce a generalized synchronization method and secure communication in the State-Controlled Cellular Neural Network (SC-CNN). We make a SC-CNN using the n-double scroll. A SC-CNN is created by applying identical n-double scroll or non-identical n-double scroll and Chua`s oscillator with weak coupled method to each cell. SC-CNN synchronization was achieved using GS(Generalized Synchronization) method between the transmitter and receiver about each state variable in the SC-CNN. In order to secure communication, we have synthesizing the desired information with a SC-CNN circuit by adding the information signal to the hyper-chaos signal using the SC-CNN in the transmitter. And then, transmitting the synthesized signal to the ideal channel, we confirm secure communication by separating the information signal and the SC-CNN signal in the receiver.
Camera Motion Parameter Estimation Technique using 2D Homography and LM Method based on Invariant Features
Cha, Jeong-Hee ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 297~301
DOI : 10.5391/IJFIS.2005.5.4.297
In this paper, we propose a method to estimate camera motion parameter based on invariant point features. Typically, feature information of image has drawbacks, it is variable to camera viewpoint, and therefore information quantity increases after time. The LM(Levenberg-Marquardt) method using nonlinear minimum square evaluation for camera extrinsic parameter estimation also has a weak point, which has different iteration number for approaching the minimal point according to the initial values and convergence time increases if the process run into a local minimum. In order to complement these shortfalls, we, first propose constructing feature models using invariant vector of geometry. Secondly, we propose a two-stage calculation method to improve accuracy and convergence by using homography and LM method. In the experiment, we compare and analyze the proposed method with existing method to demonstrate the superiority of the proposed algorithms.
Evolution of the Behavioral Knowledge for a Virtual Robot
Hwang Su-Chul ; Cho Kyung-Dal ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 302~309
DOI : 10.5391/IJFIS.2005.5.4.302
We have studied a model and application that evolves the behavioral knowledge of a virtual robot. The knowledge is represented in classification rules and a neural network, and is learned by a genetic algorithm. The model consists of a virtual robot with behavior knowledge, an environment that it moves in, and an evolution performer that includes a genetic algorithm. We have also applied our model to an environment where the robots gather food into a nest. When comparing our model with the conventional method on various test cases, our model showed superior overall learning.
Fast Optimization by Queen-bee Evolution and Derivative Evaluation in Genetic Algorithms
Jung, Sung-Hoon ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 310~315
DOI : 10.5391/IJFIS.2005.5.4.310
This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.
Fuzzy Distance Estimation for a Fish Robot
Shin, Daejung ; Na, Seung-You ; Kim, Jin-Young ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 316~321
DOI : 10.5391/IJFIS.2005.5.4.316
We designed and implemented fish robots for various purposes such as autonomous navigation, maneuverability control, posture balancing and improvement of quick turns in a tank of 120 X 120 X 180cm size. Typically, fish robots have 30-50 X 15-25 X 10-20cm dimensions; length, width and height, respectively. It is essential to have the ability of quick and smooth turning to avoid collision with obstacles or walls of the water pool at a close distance. Infrared distance sensors are used to detect obstacles, magneto-resistive sensors are used to read direction information, and a two-axis accelerometer is mounted to compensate output of direction sensors. Because of the swing action of its head due to the tail fin movement, the outputs of an infrared distance sensor contain a huge amount of noise around true distances. With the information from accelerometers and e-compass, much improved distance data can be obtained by fuzzy logic based estimation. Successful swimming and smooth turns without collision demonstrated the effectiveness of the distance estimation.
Fuzzy equivalence relations and transformations
Kim, Yong-Chan ; Kim, Young-Sun ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 322~326
DOI : 10.5391/IJFIS.2005.5.4.322
We investigate the properties of A-transformations, P-transformations and L-transformations in metric spaces, t-norms and T-fuzzy equivalence relations.
Genetically Optimized Hybrid Fuzzy Set-based Polynomial Neural Networks with Polynomial and Fuzzy Polynomial Neurons
Oh Sung-Kwun ; Roh Seok-Beom ; Park Keon-Jun ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 327~332
DOI : 10.5391/IJFIS.2005.5.4.327
We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.
Object Search Algorithm under Dynamic Programming in the Tree-Type Maze
Jang In-Hun ; Lee Dong-Hoon ; Sim Kwee-Bo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 333~338
DOI : 10.5391/IJFIS.2005.5.4.333
This paper presents the target object search algorithm under Dynamic Programming (DP) in the Tree-type maze. We organized an experimental environment with the concatenation of Y-shape diverged way, small mobile robot, and a target object. By the principle of optimality, the backbone of DP, an agent recognizes that a given whole problem can be solved whether the values of the best solution of certain ancillary problem can be determined according to the principle of optimality. In experiment, we used two different control algorithms: a left-handed method and DP. Finally we verified the efficiency of DP in the practical application using our real robot.
Parameter Selecting in Artificial Potential Functions for Local Path Planning
Kim, Dong-Hun ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 339~346
DOI : 10.5391/IJFIS.2005.5.4.339
Artificial potential field (APF) is a widely used method for local path planning of autonomous mobile robot. So far, many different types of APF have been implemented. Once the artificial potential functions are selected, how to choose appropriate parameters of the functions is also an important work. In this paper, a detailed analysis is given on how to choose proper parameters of artificial functions to eliminate free path local minima and avoid collision between robots and obstacles. Two kinds of potential functions: Gaussian type and Quadratic type of potential functions are used to solve the above local minima problem respectively. To avoid local minima occurred in realistic situations such as 1) a case that the potential of the goal is affected excessively by potential of the obstacle, 2) a case that the potential of the obstacle is affected excessively by potential of the goal, the design guidelines for selecting appropriate parameters of potential functions are proposed.
Pattern Classification of Partial Discharge Data
Kim Sung-Ho ; Bae Geum-Dong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 347~352
DOI : 10.5391/IJFIS.2005.5.4.347
PD(Partial discharges) are small electrical sparks that occur within the electric insulation of cables, transformers and windings on motors. PD analysis is a proactive diagnostic approach that uses PD measurements to evaluate the integrity of this equipment. Recently, several diagnostic algorithms for classifying the type of PD and locating the defect position have been developed. In this work, a new PD recognition system is proposed, which utilizes approximate coefficients of wavelet transform as a feature vector, furthermore, introduces bank of Elman networks to recognize the various PD phenomena. In order to verify the performance of the proposed scheme, it is applied to the simulated PD data.
Prediction of User`s Preference by using Fuzzy Rule & RDB Inference: A Cosmetic Brand Selection
Kim, Jin-Sung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 353~359
DOI : 10.5391/IJFIS.2005.5.4.353
In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users` preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).
Real Time Traffic Signal Plan using Neural Network
Choi Myeong-Bok ; Hong You-Sik ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 360~366
DOI : 10.5391/IJFIS.2005.5.4.360
In the past, when there were few vehicles on the road, the T.O.D.(Time of Day) traffic signal worked very well. The T.O.D. signal operates on a preset signal cycling which cycles on the basis of the average number of average passenger cars in the memory device of an electric signal unit. Now days, with increasing many vehicles on restricted roads, the conventional traffic light creates startup-delay time and end-lag-time. The conventional traffic light loses the function of optimal cycle. And so,
of conventional traffic cycle is not matched to the present traffic cycle. In this paper we proposes electro sensitive traffic light using fuzzy look up table method which will reduce the average vehicle waiting time and improve average vehicle speed. Computer simulation results prove that reducing the average vehicle waiting time which proposed considering passing vehicle length for optimal traffic cycle is better than fixed signal method which doesn`t consider vehicle length.
Similarity Measure Construction with Fuzzy Entropy and Distance Measure
Lee Sang-Hyuk ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 4, 2005, Pages 367~371
DOI : 10.5391/IJFIS.2005.5.4.367
The similarity measure is derived using fuzzy entropy and distance measure. By the elations of fuzzy entropy, distance measure, and similarity measure, we first obtain the fuzzy entropy. And with both fuzzy entropy and distance measure, similarity measure is obtained., We verify that the proposed measure become the similarity measure.