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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 12, Issue 6 - Dec 2002
Volume 12, Issue 5 - Oct 2002
Volume 12, Issue 4 - Aug 2002
Volume 12, Issue 3 - Jun 2002
Volume 12, Issue 2 - Apr 2002
Volume 12, Issue 1 - Feb 2002
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Optimization of Multi-objective Function based on The Game Theory and Co-Evolutionary Algorithm
Sim, Kwee-Bo ; Kim, Ji-Yoon ; Lee, Dong-Wook ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 491~496
DOI : 10.5391/JKIIS.2002.12.6.491
Multi-objective Optimization Problems(MOPs) are occur more frequently than generally thought when we try to solve engineering problems. In the real world, the majority cases of optimization problems are the problems composed of several competitive objective functions. In this paper, we introduce the definition of MOPs and several approaches to solve these problems. In the introduction, established optimization algorithms based on the concept of Pareto optimal solution are introduced. And contrary these algorithms, we introduce theoretical backgrounds of Nash Genetic Algorithm(Nash GA) and Evolutionary Stable Strategy(ESS), which is the basis of Co-evolutionary algorithm proposed in this paper. In the next chapter, we introduce the definitions of MOPs and Pareto optimal solution. And the architecture of Nash GA and Co-evolutionary algorithm for solving MOPs are following. Finally from the experimental results we confirm that two algorithms based on Evolutionary Game Theory(EGT) which are Nash GA and Co-evolutionary algorithm can search optimal solutions of MOPs.
A DNA Coding-Based Interacting Multiple Model Method for Tracking a Maneuvering Target
Lee, Bum-Jik ; Joo, Young-Hoon ; Park, Jin-Bae ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 497~502
DOI : 10.5391/JKIIS.2002.12.6.497
The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the state of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, a DNA coding-based interacting multiple model (DNA coding-based W) method is proposed. The proposed method can overcome the mathematical limits of conventional methods by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive IMM algorithm and the GA-based IMM method in computer simulations.
Neural Networks-based Statistical Approach for Fault Diagnosis in Nonlinear Systems
Lee, In-Soo ; Cho, Won-Chul ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 503~510
DOI : 10.5391/JKIIS.2002.12.6.503
This paper presents a fault diagnosis method using neural network-based multi-fault models and statistical method to detect and isolate faults in nonlinear systems. In the proposed method, faults are detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.
Design the Structure of Scaling-Wavelet Mixed Neural Network
Kim, Sung-Soo ; Kim, Yong-Taek ; Seo, Jae-Yong ; Cho, Hyun-Chan ; Jeon, Hong-Tae ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 511~516
DOI : 10.5391/JKIIS.2002.12.6.511
The neural networks may have problem such that the amount of calculation for the network learning goes too big according to the dimension of the dimension. To overcome this problem, the wavelet neural networks(WNN) which use the orthogonal basis function in the hidden node are proposed. One can compose wavelet functions as activation functions in the WNN by determining the scale and center of wavelet function. In this paper, when we compose the WNN using wavelet functions, we set a single scale function as a node function together. We intend that one scale function approximates the target function roughly, the other wavelet functions approximate it finely During the determination of the parameters, the wavelet functions can be determined by the global search for solutions suitable for the suggested problem using the genetic algorithm and finally, we use the back-propagation algorithm in the learning of the weights.
Ship s Maneuvering and Winch Control System with Voice Instruction Based Learning
Seo, Ki-Yeol ; Park, Gyei-Kark ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 517~523
DOI : 10.5391/JKIIS.2002.12.6.517
In this paper, we propose system that apply VIBL method to add speech recognition to LIBL method based on human s studying method to use natural language to steering system of ship, MERCS and winch appliances and use VIBL method to alternate process that linguistic instruction such as officer s steering instruction is achieved via ableman and control steering gear, MERCS and winch appliances. By specific method of study, ableman s suitable steering manufacturing model embodies intelligent steering gear controlling system that embody and language direction base studying method to present proper meaning element and evaluation rule to steering system of ship apply and respond more efficiently on voice instruction of commander using fuzzy inference rule. Also we embody system that recognize voice direction of commander and control MERCS and winch appliances. We embodied steering manufacturing model based on ableman s experience and presented rudder angle for intelligent steering system, compass bearing arrival time, evaluation rule to propose meaning element of stationary state and correct steerman manufacturing model rule using technique to recognize voice instruction of commander and change to text and fuzzy inference. Also we apply VIBL method to speech recognition ship control simulator and confirmed the effectiveness.
Stepwise Fuzzy Moving Sliding Surface for Second-Order Nonlinear Systems
Yoo, Byung-Kook ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 524~530
DOI : 10.5391/JKIIS.2002.12.6.524
This note suggests a stepwise fuzzy moving sliding surface using Sugeno-type fuzzy system and presents a sliding mode control scheme using it. The fuzzy system has the angle of state error vector and the distance from the origin in the phase plane as inputs and a first-order linear differential equation as output. The surface initially passes arbitrary initial states and subsequently moves towards a predetermined surface via rotating or shifting. This method reduces the reaching and tracking time and improves robustness. Conceptually the slope of the Proposed fuzzy moving sliding surface increases stepwise in the stable region of the phase plane. The surface, however, rotates continuously because the surface is a fuzzy system. The asymptotic stability of the fuzzy sliding surface is proved. The validity of the proposed control scheme is shown in computer simulation for a second-order nonlinear system.
The Intelligent Intrusion Detection Systems using Automatic Rule-Based Method
Yang, Ji-Hong ; Han, Myung-Mook ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 531~536
DOI : 10.5391/JKIIS.2002.12.6.531
In this paper, we have applied Genetic Algorithms(GAs) to Intrusion Detection System(TDS), and then proposed and simulated the misuse detection model firstly. We have implemented with the KBD contest data, and tried to simulated in the same environment. In the experiment, the set of record is regarded as a chromosome, and GAs are used to produce the intrusion patterns. That is, the intrusion rules are generated. We have concentrated on the simulation and analysis of classification among the Data Mining techniques and then the intrusion patterns are produced. The generated rules are represented by intrusion data and classified between abnormal and normal users. The different rules are generated separately from three models "Time Based Traffic Model", "Host Based Traffic Model", and "Content Model". The proposed system has generated the update and adaptive rules automatically and continuously on the misuse detection method which is difficult to update the rule generation. The generated rules are experimented on 430M test data and almost 94.3% of detection rate is shown.3% of detection rate is shown
Metrics for Maintainability of Class Inheritance Structures and it지s Evaluation
Chung, Hong ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 537~542
DOI : 10.5391/JKIIS.2002.12.6.537
We propose new metrics for understandability and modifiability of class inheritance structures based on the object-oriented metrics suggested by Chidamber and Kemerer. The metrics are evaluated using the results of Gursaran s experiments which validated the empirical relation of DIT(Depth of Inheritance Tree) and NOC(Number of Children) metrics of Chidamber and Kemerer.
Majority-Voting FCM with Implied Validity Measure
Lee, Gang-Hwa ; Lee, Dong-Il ; Lee, Suk-Gyu ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 543~548
DOI : 10.5391/JKIIS.2002.12.6.543
It is well known that FCM is an indispensible tool for fuzzy clustering. The problems of using FCM are 1) it is sensitive to the initial random membership functions and 2) FCM inherently requires the number of clusters. Hence we need to run FCM algorithms with an appropriate validity measure until we find a suitable number of clusters. In this paper, we suggest the Majority-Voting FCM with implied validity measure. With this algorithm, we can solve the aforementioned problems. The working simulation results are provided. The contributions are 1) MV-FCM algorithm and 2) its definitive capability of being an excellent validity measure.
A Knowledge-Based Linguistic Approach for Researcher-Selection
Lim, Joon-Shik ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 549~553
DOI : 10.5391/JKIIS.2002.12.6.549
This paper develops knowledge-based multiple fuzzy rules for researcher-selection by automatic ranking process. Inference rules for researcher-selection are created, then the multiple fuzzy rule system with max-min inference is applied. The way to handle for selection standards according to a certain criteria in dynamic manner, is also suggested in a simulation model. The model offers automatic, fair, and trust decision for researcher-selection processing.
A Robust Indirect Adaptive Fuzzy State Feedback Regulator Based on Takagi-Sugeno Fuzzy Model
Hyun, Chang-Ho ; Park, Chang-Woo ; Park, Mignon ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 554~558
DOI : 10.5391/JKIIS.2002.12.6.554
In this paper, we propose a robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model. The proposed adaptive fuzzy regulator is less sensitive to singularity than the conventional one based on the feedback linearization method. Furthermore, the proposed control method is applicable to not only plants with a perfect model but also plants with an imperfect model, which causes uncertainties. We verify the global stability of the proposed method by using Lyapunov method. In order to support the achievement, the application of the proposed adaptive fuzzy regulator to the control of a nonlinear system under the external disturbance is presented and the performance was verified by some simulation result.
Design of Fuzzy Output Feedback Controller for The Nonlinear Systems with Time -Delay
Shin, Hyun-Seok ; Kim, Eun-Tai ; Park, Mignon ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 559~564
DOI : 10.5391/JKIIS.2002.12.6.559
This Paper Proposes a design method of a fuzzy output feedback controller for the nonlinear systems with the unknown time- delay. Recently, Cao et ai. proposed a stabilization method for the nonlinear time-delay systems using a fuzzy controller when the time-delay is known. However, the time-delay is likely to be unknown in practical. We represent the nonlinear systems with the unknown time-delay by Takagi-Sugeno (T-5) fuzzy model and design the fuzzy observer and the parallel distributed compensation (PDC) law based on this observer. By applying Lyapunov-Krasovskii theorem to the closed-loop system, the sufficient condition for the asymptotic stability of the equilibrium Point is derived and converted into the linear matrix inequality (LMI) Problem.
Fuzzy H-continuous Mappings and Fuzzy Strongly Closed Graphs
Hur, K. ; Ahn, Y.S. ; Ryou, J.H. ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 565~570
DOI : 10.5391/JKIIS.2002.12.6.565
We introduce the concepts of fuzzy H-continuity and fuzzy strongly closed graph, respectively and investigate some of their properties.
A Neuro-Fuzzy System Modeling using Gaussian Mixture Model and Clustering Method
Kim, Sung-Suk ; Kwak, Keun-Chang ; Ryu, Jeong-Woong ; Chun, Myung-Geun ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 571~576
DOI : 10.5391/JKIIS.2002.12.6.571
There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.
Neural-based Blind Modeling of Mini-mill ASC Crown
Lee, Gang-Hwa ; Lee, Dong-Il ; Lee, Seung-Joon ; Lee, Suk-Gyu ; Kim, Shin-Il ; Park, Hae-Doo ; Park, Seung-Gap ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 577~582
DOI : 10.5391/JKIIS.2002.12.6.577
Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.
On entropy for intuitionistic fuzzy sets applying the Euclidean distance
Hong, Dug-Hun ;
Journal of Korean Institute of Intelligent Systems, volume 12, issue 6, 2002, Pages 583~588
DOI : 10.5391/JKIIS.2002.12.6.583
Recently, Szmidt and Kacprzyk[Fuzzy Sets and Systems 118(2001) 467-477] proposed a non-probabilistic-type entropy measure for intuitionistic fuzzy sets. Tt is a result of a geometric interpretation of intuitionistic fuzzy sets and uses a ratio of distances between them. They showed that the proposed measure can be defined in terms of the ratio of intuitionistic fuzzy cardinalities: of
, while applying the Hamming distances. In this note, while applying the Euclidean distances, it is also shown that the proposed measure can be defined in terms of the ratio of some function of intuitionistic fuzzy cardinalities: of