• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.60-65
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• 2000

We propose a new method to deal with the optimal gain self-tuning of the PID controller which is used to industrial process control in various fields. First of all, in this method, first order delay system which was modeled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nickels method. Finally, we can find the parameters of Pm controller so as to maximize the fuzzy inferencl function which includes the maximum overshoot, damping ratio, rising time and settling time. The proposed method also shows good adaptability for variations in characteristics and dead time of the system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1182-1189
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• 1994

In this paper, we evaluate the performance of a fuzzy controller with a self-learning structure. The fuzzy controller is based on a fuzzy logic that approximates and effectively represents the uncertain phenomena of the real world. The fuzzy controller has control of a plant with a fuzzy inference logic. However, it is not easy to decide the membership function of a fuzzy controller and its controlrule. This problem can be solved by designing a self-learning controller that improves its own contropllaw to its goal with a performance table. The fuzzy controller is implemented with a 386PC, an interface board, a D/A converter, a PWM(Pulse Width Modulation) motor drive-circuit, and a sensing circuit, for error and differential of error. Since a Ball and Beam System is used in the experiment, the validity of the fuzzy controller with the self-learning structure can be evaluated through the actual experiment and the computer simulation of the real plant. The self-learning fuzzy controller reduces settling time by just under 10%.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.842-848
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• 2008

This paper deal with uncertainty problem by using Type-2 fuzzy logic set for nonlinear system modeling. We design Type-2 fuzzy logic system in which the antecedent and the consequent part of rules are given as Type-2 fuzzy set and also analyze the performance of the ensuing nonlinear model with uncertainty. Here, the apexes of the antecedent membership functions of rules are decided by C-means clustering algorithm and the apexes of the consequent membership functions of rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The proposed model is demonstrated with the aid of two representative numerical examples, such as mathematical synthetic data set and Mackey-Glass time series data set and also we discuss the approximation as well as generalization abilities for the model.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.355-367
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• 2007

This paper proposes a design method of the PI(Proportional-Integral)+D(Derivative) controller using self-tuning of the design variables and controller gains. The used fuzzy PI+D controller is the approximated conventional continuos time linear PI+D controller and the used fuzzification method is the fuzzy single tone and the adapted defuzzification method is the simplified tenter of gravity. Fuzzy estimation result would be calculated in the other function elements from the classified fuzzy variables and the result determined by the design variables decides the controller gains. As a result, the proposed method shows the capability of the high speed tuning and can be applied to the case of input variables with many fuzzy partitions and also can bring out the advantage to reduce the reconstruction(digital sampling reconstruction) error. Most simulation results show that this controller makes much bettor efficiency and improvement by using design variables and controller gains.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.83-93
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• 2000

This paper proposes the use of the analytic hierarchy process(AHP) controller for obtaining optimal cycle time of traffic intersection signal. By means of simulations that have data from expert traffic estimators, the performance of AHP controller is compared with a fixed-time controller and fuzzy controller. A simulation was executed using data obtained from many expert interviewer of Ansan City hall rotary. The principal issue is how AHP controller algorithm can aid traffic signal supervisor to construct an expert system for traffic intersection signal control. This paper has two steps: (I) we shall present a new methodology based on the work of Satty for calculating a scale of importance of each of the objectives, (2) we also consider that the human's decision mechanism is represented by approximating reasoning where the fuzzy integral is used.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.262-268
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• 2010

In this paper, this study restores the distorted image to its original image by compensating for the distortion of image from a fixed-focus camera lens. The various developments and applications of the imaging devices and the image sensors used in a wide range of industries and expanded use, but due to the needs of the small size and light weight of the camera, the distortion from acquiring images of the distorted curvature of the lens tends to affect many. In particular, the three-dimensional imaging camera, each different distortion of left and right lens cause the degradation of three-dimensional sensitivity and left-right image distortion ratio. we approached the way of generalizing the approximate equations to restore each part of left-right camera images to the coordinators of the original images. The adaptive Neuro-Fuzzy Inference System is configured for it. This system is divided from each membership function and is inferred by 1st order Sugeno Fuzzy model. The result is that the compensated images close to the left, right original images. Using low-cost and compact imaging lens by which also determine the exact three-dimensional image-sensing capabilities and will be able to expect from this study.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

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.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.8-17
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• 2017

Infrastructures such as bridges and tunnels are crucial elements of national economic growth, and sudden collapses may lead to great catastrophes with significant social and economic losses, as well as a loss of lives. Hence, an efficient maintenance technique must be applied to guarantee safety, secure budgets to maintain a certain level of service, and prevent maintenance expenditures from being concentrated in a specific time period. Developed countries have experienced rapid increases in maintenance budgets, and maintenance costs now account for about 40% of the total maintenance budget. The level of service in asset management systems is an essential element for setting management goals and making priority decisions. Therefore, this study uses fuzzy theory to develop a new way to assess the level of service.The assessment model was applied to an actual bridge to evaluate the level of service for users.