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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 11, Issue 9 - Dec 2001
Volume 11, Issue 8 - Dec 2001
Volume 11, Issue 7 - Dec 2001
Volume 11, Issue 6 - Dec 2001
Volume 11, Issue 5 - Oct 2001
Volume 11, Issue 4 - Aug 2001
Volume 11, Issue 3 - Jun 2001
Volume 11, Issue 2 - Apr 2001
Volume 11, Issue 1 - Feb 2001
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Reliability Computation of Neuro-Fuzzy Models : A Comparative Study
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 293~301
This paper reviews three methods to compute a pointwise confidence interval of neuro-fuzzy models and compares their estimation perfonnanee through simulations. The eOITl.putation methods under consideration include stacked generalization using cross-validation, predictive error bar in regressive models, and local reliability measure for the networks employing a local representation scheme. These methods implemented on the neuro-fuzzy models are applied to the problems of simple function approximation and chaotic time series prediction. The results of reliability estimation are compared both quantitatively and qualitatively.
2D Face Image Recognition and Authentication Based on Data Fusion
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 302~306
Because face Images have many variations(expression, illumination, orientation of face, etc), there has been no popular method which has high recognition rate. To solve this difficulty, data fusion that fuses various information has been studied. But previous research for data fusion fused additional biological informationUingerplint, voice, del with face image. In this paper, cooperative results from several face image recognition modules are fused without using additional biological information. To fuse results from individual face image recognition modules, we use re-defined mass function based on Dempster-Shafer s fusion theory.Experimental results from fusing several face recognition modules are presented, to show that proposed fusion model has better performance than single face recognition module without using additional biological information.
A Measure of typicality of a fuzzy set with respect to a random sample
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 307~310
A measure of typicality associated with any fuzzy subset on a probability space is introduced. We will improve some results of Yager and consider limit of the measure of typicality of a fuzzy set with ,respect to a random sample.
MVL-Automata for General Purpose Intelligent Model
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 311~314
Recently, research on Intelligent Information Process has actively been under way JD various areas and gradually extended to be adaptive to uncertain and complex dynamic environments. This paper presents a Multiple Valued Logic Automata(MVL-Automata) Model, utilizing properties of difference in a Multiple Valued Logic function. That is, MVL-Automata is able to be autonomously adapted to dynamic changing since an input stling is mapped to the value of a Multiple Valued Logic function and the property of difference in a Multiple Valued Logic function is applied to state transition. Therefore, Multiple Valued Logic Automata can be widely applied to the modeling dynamically of changing environments.
A Study on Chaotic Phenomenon in Rolling Mill Bearing
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 315~319
A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide e8rly w, ul1ings in rolling mill. Because dynamics of rolling mill is non-linear. This paper shows a chaotic behaviour of vibration signal in rolling mill using embedding method. Phase plane and Poincare map, FFT and histogram of vibration signal in rolling mill are implemented by qualitative analysis and Fractal dimension, Lyapunov exponent are presented by quantitative analysis.
Self-Change Detection Algorithms using the Artificial Immune System
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 320~324
According to the rapid growth of computer and internet recently, A hacking to steal infonnations and the computer vinls to destroy the data in computer are now prevailing in the whole world. A study of methods to protect the data of computer is in progress. One of the study is constmction of computer immune system using biological immune system tbat has ability of removal and protection from extemal invasion. In this paper, we make a change detection algorithm which is based on ability of distinction between self and nonself in T-cytotoxic cell that is one of biological immune cell. In algorithm, MHC receptors are composed of a part of self-file that is recognized as itself and those shall distinguish self-file from the changed file. As a result of applying this algorithm to the changed self-files, we prove the efficacy of detection of the self-files changed by computer virus and hacking.
Global Convergence of Neural Networks for Optimization
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 325~330
It has been realized that the results of circuit level simulation of neural networks, used for optimization problems, arc much different from those of algorism level simulation. In other words, the outputs converges asymptotically as time elapes, however, the input convergence depends on the value of parasitic conductance connected between input node and ground. Also, this conductance affects system performance. This paper discusses the influence of input conductance on the convergece of the continuous Hopfield neural networks. The convergence has been analyzed for the input and output nodes of neurons. Also, the characteristics of equilibrium points has been analyzed depending on different values of the input conductance.
CMAC Neuro-Fuzzy Design for Color Calibration
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 331~335
Cl\iAC model was proposed by Albus [6J to formulate the processing characteristics of the human cerebellum. Instead of the global weight updating scheme used in the back propagation, CMAC use the local weight updating scheme. Therefore, CMAC have the advantage of fast learning and high convergence rate. In this paper, simulate Color Calibration by CMAC in color images and design hardware by VHDL-base high-level synthesis.
Forecasting High-Level Ozone Concentration with Fuzzy Clustering
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 336~339
The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Especially, the performance of the prediction results in the high-level ozone concentration are not good. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering methods. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, the identification of nonlinear complex systems, and prediction of dynamical systems.
A New Design Method for the GBAM (General Bidirectional Associative Memory) Model
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 340~346
This paper proposes a new design method for the GBAM: (general bidirectional associative memory) model. Based on theoretical investigations on the GBAM: model, it is shown that the design of the GBAM:-based bidirectional associative memeories can be formulated as optimization problems called GEVPs (generalized eigenvalue problems). Since the GEVPs arising in the procedure can be efficiently solved within a given tolerance by the recently developed interior point methods, the design procedure established in this paper is very useful in practice. The applicability of the proposed design procedure is demonstrated by simple design examples considered in related studies.
Stability of TSK-type Time-Delay FLC
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 347~353
A stable TSK -type FLC can be designed by the method of Parallel Distributed Compensation (PDC)  but in this case, solving the LMI problem is not a trivial task. To overcome such a difficulty, a Time-Delay based FLC (TDFLC) is proposed. TSK -type TDFLC consists of Time-Delay Control (TDC) and Sliding Mode Control (SMC) schemes, which result in a robust controller based upon an integral sliding surface. Finally, simulation study is conducted for a mass-spring-damper system.
A Fuzzy Neural Network Model Solving the Underutilization Problem
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 354~358
This paper presents a fuzzy neural network model which solves the underutilization problem. This fuzzy neural network has both stability and flexibility because it uses the control structure similar to AHT(Adaptive Resonance Theory)-l neural network. And this fuzzy nenral network does not need to initialize weights and is less sensitive to noise than ART-l neural network is. The learning rule of this fuzzy neural network is the modified and fuzzified version of Kohonen learning rule and is based on the fuzzification of leaky competitive leaming and the fuzzification of conditional probability. The similarity measure of vigilance test, which is performed after selecting a winner among output neurons, is the relative distance. This relative distance considers Euclidean distance and the relative location between a datum and the prototypes of clusters. To compare the performance of the proposed fuzzy neural network with that of Kohonen Self-Organizing Feature Map the IRIS data and Gaussian-distributed data are used.
The Design of Target Tracking System Using FBFE Based on VEGA
Journal of Korean Institute of Intelligent Systems, volume 11, issue 4, 2001, Pages 359~365
In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion(FBFE) based on virus evolutionary genetic algorithm (VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FDFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by idenLifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.