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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 9, Issue 6 - Dec 1999
Volume 9, Issue 5 - Oct 1999
Volume 9, Issue 4 - Aug 1999
Volume 9, Issue 3 - 00 1999
Volume 9, Issue 2 - 00 1999
Volume 9, Issue 1 - 00 1999
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The Speed Control of Vector controlled Induction Motor Based on Neural Networks
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 463~471
This paper presents a vector controlled induction motor is implemented by neural networks system compared with PI controller for the speed control. The design employed the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE) for speed. In order to update the weights of the controller First of all Emulator updates its parameters by identifying the motor input and output next it supplies the error path to the output stage of the controller using backpropagation algorithm, As Controller produces an adequate output to the system due to neural networks learning capability Vector controlled induction motor characteristics actual motor speed with based on neural network system follows the reference speed better than that of linear PI speed controller.
An Implementation of Neuro-Fuzzy Based Land Convert Pattern Classification System for Remote Sensing Image
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 472~479
In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.
Design and experiment of fuzzy PID yaw rate controller for an electrically driven four wheel vehicle without steering mechanism
I, H ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 480~489
Design and experimental results of yaw rate controller is described for electricallydriven four wheel vehicle without steering mechanism. Yaw rate controller has been known to be necessary to cope with nonlinear char-acteristics of the wheel/road conditions with respect to different road condition and steering angle. For an effective yaw rate control, a fuzzy PID gain scheduler is considered with changing control parameters. In order to apply proposed algorithm to the system a downsized four wheel drive electrically driven vehicle without steering mechanism was manufactured. With these techniques the proposed yaw rate controller is shown by experiment results to be obtained suficient performance in the whole steering regions.
A Design Method for Error Backpropagation neural networks using Voronoi Diagram
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 490~495
In this paper. a learning method VoD-EBP for neural networks is proposed, which learn patterns by error back propagation. Based on Voronoi diagram, the method initializes the weights of the neural networks systematically, wh~ch results in faster learning speed and alleviated local optimum problem. The method also shows better the reliability of the design of neural network because proper number of hidden nodes are determined from the analysis of Voronoi diagram. For testing the performance, this paper shows the results of solving the XOR problem and the parity problem. The results were showed faster learning speed than ordinary error back propagation algorithm. In solving the problem, local optimum problems have not been observed.
Design of The Robust Fuzzy Controller Using State Feedback Gain
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 496~508
Fuzzy System which are based on membership functions and rules can control nonlinear uncertain complex systems well. However Fuzzy logic controller(FLC) has problems; It is difficult to design the stable FLC and FLC depends mainly on individual experience. Although FLC can be designed using the error back-propagation algorithm it takes long time to converge into global optimal parameters. Well-developed linear system theory should not be replaced by FLC but instead it should be suitably used with FLC. A new methodology is introduced for designing THEN-PART membership functions of FLC based on its well-tuned state feedback controller. A example of inverted pendulum is given for demonstration of the robustness of proposed methodology.
Semiprime and Semiprimary Fuzzy Ideals
Jeong, Tae-Eun ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 509~512
We study semiprime fuzzy ideals semiprimary fuzzy ideals and their properties. We investigate that if a fuzzy ideal is semiprime and semiprimary then it is prime.
On fuzzy pairwise irresolute mappings
Im, Young-Bin ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 513~516
In this paper we further investigate some proterties of fuzzy pairwise irresolute mappings of fuzzy bitopological spaces.
An Efficient Classifying Recognition Algorithm of Printed and handwritten numerals
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 517~525
In this paper, we propose efficient total recognition system of handwritten and printed numerals for reducing the classification time. The proposed system consists of two-step neuroclassifier : Printed numerals classifier and handwritten numerals classifier. In the proposed scheme, the printed numerals classifier classifies the printed numerals rapidly with single MLP neural network by low-order feature vector and rejects handwritten numerals. The handwritten numerals classifier classifies the handwritten numerals which is rejected in printed numerals classifier with modularized cluster neural network by complex feature vector. In order to verify the performance of the proposed method,handwritten numerals database of NIST and printed numerals database which include various fonts are used in the experiments. In case of using the proposed classifier, the overall classification time was reduced by 49.1% - 65.5% in comparison of the existent handwritten classifier.
A Study on the Emotional Evaluation Model of Color Pattern Based on Adaptive Fuzzy System
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 526~537
In the paper. we propose an evaluation model based the adaptive fuzzy systems, which can transform the physical features of a color pattern to the emotional features. The model is motivated by the Soen's psychological experiments, in which he found the physical features such as average hue, saturation, intensity and the dynamic components of the color patterns affects to the emotional features represented by a pair of adjective words having the opposite meanings. Our proposed model consists of two adaptive fuzzy rule-bases and the y-model, a l i r ~ r ys et operator, to fuze the evaluation values produced by them. The model shows con~parablep erformances to the neural network for the approximation of the nonlinear transforms, and it has the advantage to obtain the linbwistic interpretation from the trained results. We believe the evaluated results of a color pattern can be used to the emotion-based color image retrievals.
Fault Detection Relaying for Transmission line Protection using ANFIS
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 538~544
In this paper, we propose a new fault detection algorithm for transmission line protection using ANFIS(Adaptive Network Fuzzy Inference System). The developed system consists of two subsystems: fault type classification, and fault location estimation. We use rms value, zero sequence component and positive sequence of current, and then using learning method of neural network, premise and consequent parameters are tuned properly. To prove the performance of the proposcd system, generated data by EMTP(Electr0- Magnetic Transient Program) sin~ulationi s used. It is shown that the proposed relaying classifies fault types accurately and advances fault location estimation.
A Study on Extracting Chaotic Properties from High Impedance Faults in Power Systems
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 545~549
Previous studies on high impedance faults assumed that the erratic behavior of fault current would be random. In this paper we prove that the nature of the high impedance faults is indeed a deterministic chaos not a random motion. Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents in order to evaluate the orbital instability peculiar to deterministic chaos dynamically and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. In addition qualitative analysis such a s phase planes Poincare maps obtained from fault currents indicate that the irregular behavior is described by strange attractor.
Fuzzy closure spaces and fuzzy quasi-proximity spaces
Lee, Jong-Wan ;
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 550~554
We will define a fuzzy quasi-proximity space and give some examples of it. We show that the family M(X, C) of all fuzzy quasi-proximities on X which induce C is nonempty. Moreover we will study the relationship between the category of fuzzy closure spaces and that of fuzzy quasi-proximity spaces.
A Study on Optimal fuzzy Systems by Means of Hybrid Identification Algorithm
Journal of Korean Institute of Intelligent Systems, volume 9, issue 5, 1999, Pages 555~565
The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.