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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 10, Issue 6 - Dec 2000
Volume 10, Issue 5 - Oct 2000
Volume 10, Issue 4 - Aug 2000
Volume 10, Issue 3 - Jun 2000
Volume 10, Issue 2 - Apr 2000
Volume 10, Issue 1 - 00 2000
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Neuro-Fuzzy Controller Based on Reinforcement Learning
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 395~400
In this paper, we propose a new neuro-fuzzy controller based on reinforcement learning. The proposed system is composed of neuro-fuzzy controller which decides the behaviors of an agent, and dynamic recurrent neural networks(DRNNs) which criticise the result of the behaviors. Neuro-fuzzy controller is learned by reinforcement learning. Also, DRNNs are evolved by genetic algorithms and make internal reinforcement signal based on external reinforcement signal from environments and internal states. This output(internal reinforcement signal) is used as a teaching signal of neuro-fuzzy controller and keeps the controller on learning. The proposed system will be applied to controller optimization and adaptation with unknown environment. In order to verifY the effectiveness of the proposed system, it is applied to collision avoidance of an autonomous mobile robot on computer simulation.
Design and Implementation of The Feedback Fuzzy Controller
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 401~408
In this paper, we proposed a fuzzy controller that founded by the general feedback control with the new adjustment method when it's tuning. The general feedback controller is operated that supply to the plant making the control input multiplying the appropriate gain of controller on the error between the output of the plant and the reference, But proposed feedback fuzzy controller consist of three loops. The inner loop consists of plant and an ordinary feedback controller. The fuzzy inference of controller performed by the outer loops, which is composed of a fuzzy modeling and inference. We can observe that the output of control system converges toward the reference. Also, the behaviour of feedback fuzzy system is converged from the transient. That is, we verified that designed fuzzy controllers was adapted effectively through the experiments in the hydraulic motor system using floating point DSP processor.
Fuzzy-based Processor Allocation Strategy for Multiprogrammed Shared-Memory Multiprocessors
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 409~416
In the shared-memory mutiprocessor systems, shared processing techniques such as time-sharing, space¬sharing, and gang-scheduling are used to improve the overall system utilization for the parallel operations. Recently, LLPC(Loop-Level Process Control) allocation technique was proposed. It dynamically adjusts the needed number of processors for the execution of the parallel code portions based on the current system load in the given job. This method allocates as many available processors as possible, and does not save any processors for the parallel sections of other later-arriving applications. To solve this problem, in this paper, we propose a new processor allocation technique called FPA(Fuzzy Processor Allocation) that dynamically adjusts the number of processors by fuzzifYing the amounts ofueeded number of processors, loads, and estimated execution times of job. The proposed method provides the maximum possibility of the parallism of each job without system overload. We compare the performances of our approaches with the conventional results. The experiments show that the proposed method provides a better performance
Nonlinear System Modeling using Independent Component Analysis and Neuro-Fuzzy Method
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 417~422
In this paper, an efficient fuzzy rule generation scheme for adaptive neuro-fuzzy system modeling using the Independent Component Analysis(ICA) as a preprocessing is proposed. Correlation between inputs was not considered in the conventional neuro- fuzzy modeling schemes, such that enormous number of rules and large amount of error were unavoidable. The correlation between inputs is weakened by employing ICA so that the number of rules and the amount of error are reduced. In simulation, the Box-Jenkins furnace data is used to verify the effectiveness of the proposed method.
Real-Time Digital Fuzzy Control Systems considering Computing Time-Delay
Park, Chang-Woo ; Shin, Hyun-Seok ; Park, Mig-Non ;
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 423~431
In this paper, the effect of computing time-delay in the real-time digital fuzzy control systems is investigated and the design methodology of a real-time digital fuzzy controller(DFC) to overcome the problems caused by it is presented. We propose the fuzzy feedback controller whose output is delayed with unit sampling period. The analysis and the design problem considering computing time-delay is very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy control system is solved by the linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to find the stable feedback gains and a common positive definite matrix P for the designed fuzzy control system Furthermore, we develop a real-time fuzzy control system for backing up a computer-simulated truck-trailer with the consideration of the computing time-delay. By using the proposed method, we design a DFC which guarantees the stability of the real time digital fuzzy control system in the presence of computing time-delay.
Fuzzy Modeling Using Virus-Evolutionary Genetic Algorithm
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 432~441
This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Genetic algorithm has been used to identifY parameters and structure of fuzzy model because it has the ability to search optimal solution somewhat globally. The genetic algorithm, however, has a problem, which optimization process can be premature convergence in the case of lack of genetic divergence of population. Virus- evolutionary genetic algorithm(VEGA) could be a strategy against this local convergence. Therefore, we use VEGA for fuzzy modeling. In this method, local information is exchanged in population so that population can sustain genetic divergence. finally, to prove the theoretical hypothesis, we provide numerical examples to evaluate the feasibility and generality of fuzzy modeling using VEGA.
The State Space Identification Model of the Dynamic System using Neural Networks
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 442~448
The conventional control of dynamic systems needs accurate mathematical modeling of control systems. But the modeling of dynamic systems require very complex computation process due to complex state equation and many control parameters. Accordingly this paper proposes a state space identification model of the dynamic system using neural networks. The Gauss-Newton method is used to train the proposed neural network and the effectiveness of proposed method is verified through the computer simulation of the Seesaw system identification problem.
A Study on a Neural Network-Based Feed Identification Method in Crude Distillation Unit
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 449~458
In this paper, we propose a feed identification method using neural network to predict feed in crude distillation unit. The proposed FINN(feed identifier by neural network) is functionally composed of two modes-training mode and prediction mode. Also, we implement a neural network-based soft sensor system using Borland C++(3.0) Builder. The effectiveness of the proposed neural network-based feed identification method is shown by simulation results
Focus Control for CCD Camera using Annealing Algorithm
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 459~465
In this paper, we propose a method for controlling camera focus in the short distance by analyzing NTSC signal of a CCD camera. When the distance between a camera and an object is less than about 1 meter, the existing CCD cameras with auto-focusing function are hard to acquire the proper images because they focus on the protruding minute parts ofthe object without taking into account the whole state of the object. To solve such a problem, we use an annealing algorithm to control the motor of a camera by analyzing the overall signal obtained from the camera. By doing so, we can acquire the adequate images at the near distance. The proposed method will be used for a personal identification system by human iris patterns
CMAC Controller with Adaptive Critic Learning for Cart-Pole System
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 466~477
For developing a CMAC-based adaptive critic learning system to control the cart-pole system, various papers including neural network based learning control schemes as well as an adaptive critic learning algorithm with Adaptive Search Element are reviewed and the adaptive critic learning algorithm for the ASE is integrated into a CMAC controller. Also, quantization problems involved in integrating CMAC into ASE system are studied. By comparing the learning speed of the CMAC system with that of the ASE system and by considering the learning genemlization of the CMAC system with the adaptive critic learning, the applicability of the adaptive critic learning algorithm to CMAC is discussed.
Designing Circuits for Low Power using Genetic Algorithms
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 478~486
This paper proposes a design method that can minimize the power dissipation of CMOS digital circuits without affecting their optimal operation speeds. The proposed method is based on genetic algorithms(GAs) combined to the retiming technique, a circuit transformation technique of repositioning flip-flops. The proposed design method consists of two phases: the phase of retiming for optimizing clock periods and the phase of GA retiming for minimizing power dissipation. Experimental results using Synopsys Design Analyzer show that the proposed design method can reduce the critical path delay of example circuits by about 30-50% and improve the dynamic power performance of the circuits by about 1.4~18.4%.
Multi-FNN Identification by Means of HCM Clustering and ITs Optimization Using Genetic Algorithms
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 487~496
In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.
Cyber Shopping Mall Design and Implementation using Intelligent Sale Agent
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 497~505
Today people are very interested in an electronic commerce based on web according to the rapid growth of Internet and multimedia technology. Buyers want the special services for themselves as they become more reasonable, wiser and deepening the tendency of personality. But today most electronic commerce only serves the catalog of goods which buyers see and choose shapes and standards of goods. It is needed sale agent using sale clerks' knowledge beyond the level of service only offering the information about goods to satisfY buyers. So in this thesis buyers can buy the goods suiting buyers' taste using ISA(lntelligent Sale Agent), sale clerks in cyber in place of sale clerks in actual shops in real world. The use of this kind of intelligent sale agent makes buyers save the time for searching for goods and do shopping suiting buyers' taste.
Learning Rules for AMR of Collision Avoidance using Fuzzy Classifier System
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 506~512
In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modifY rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verifY the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.
Fuzzy r-convergent nets
Kim, Yong-Chan ; Kim, Young-Sun ;
Journal of Korean Institute of Intelligent Systems, volume 10, issue 5, 2000, Pages 513~519
In this paper, we investigate some properties of fuzzy r-cluster points and fuzzy r-limit points in smooth fuzzy topological spaces. We define fuzzy r-convergent nets and investigate some of their properties.