• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.958-961
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• 2001

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

• Journal of Korean Society for Quality Management
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• v.27 no.3
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• pp.125-141
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• 1999

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability with Poisson prior information are studied. For model selection, we explored the relative error.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.370-370
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• 2000

In this paper, we design a Multi-Fuzzy model by means of HCM clustering and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy ate identified by 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. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy mode] and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3007-3009
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• 2000

In this paper, we design a Multi-Fuzzy model by means of clustering method and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model. HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.27-40
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• 2022

In this paper, the focus on the removal noise in the binary image based on the variational Bayesian method with the Ising model. The observation and the latent variable are the degraded image and the original image, respectively. The posterior distribution is built using the Markov random field and the Ising model. Estimating the posterior distribution is the same as reconstructing a degraded image. MCMC and variational Bayesian inference are two methods for estimating the posterior distribution. However, for the sake of computing efficiency, we adapt the variational technique. When the image is restored, the iterative method is used to solve the recursive problem. Since there are three model parameters in this paper, restoration is implemented using the VECM algorithm to find appropriate parameters in the current state. Finally, the restoration results are shown which have maximum peak signal-to-noise ratio (PSNR) and evidence lower bound (ELBO).

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.141-150
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• 2000

There are three significant drawbacks in extant fuzzy rule-based expert system languages. First, they lack the functionality of composite object inference. Second, they do not support fuzzy reasoning semantically easy to understand and conceptually simple to use. Third, knowledge representation and reasoning style of their model have a great semantic gap with those of current database models. Therefore, it is very difficult for the two models to be seamlessly integrated with each other. This paper provides the formal specification of a fuzzy object inference model to solve the three drawbacks. GIS(Geographic Information System) application domain is used to demonstrate that our model naturally models complex GIS information in terms of composite objects and successfully performs fuzzy inference between them.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.119-136
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• 1996

The evaluation of risk level or possibility of traffic accidents is a fundamental task in reducing the dangers associated with current transportation system. However, due to the lack of data and basic researches for identifying such factors, evaluations so far have been undertaken by only the experts who can use their judgements well in this regard. Here comes the motivation this thesis to evaluate such risk level more or less in an automatic manner. The purpose of this thesis is to test the fuzzy-logic theory in evaluating the risk level of traffic accidents. In modeling the process of expert's logical inference of risk level determination, only the geometric features have been considered for the simplicity of the modeling. They are the visibility of road surface, horizontal alignment, vertical grade, diverging point, and the location of pedestrain crossing. At the same time, among some inference methods, fuzzy composition inference method has been employed as a back-bone inference mechanism. In calibration, the proposed model used four sites' data. After that, using calibrated model, six sites' risk levels have been identified. The results of the six sites' outcomes were quite similar to those of real world other than some errors caused by the enforcement of the model's output. But it seems that this kind of errors can be overcome in the future if some other factors such as driver characteristics, traffic environment, and traffic control conditions have been considered. Futhermore, the application of site's specific time series data would produce better results.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.685-699
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• 2006

Commercial banks and other related areas have developed internal models to better quantify their financial risks. Since an appropriate credit risk model plays a very important role in the risk management at financial institutions, it needs more accurate model which forecasts the credit losses, and statistical inference on that model is required. In this paper, we propose a new method for estimating a default rate. It is a Bayesian approach using the power prior which allows for incorporating of historical data to estimate the default rate. Inference on current data could be more reliable if there exist similar data based on previous studies. Ibrahim and Chen (2000) utilize these data to characterize the power prior. It allows for incorporating of historical data to estimate the parameters in the models. We demonstrate our methodologies with a real data set regarding SOHO data and also perform a simulation study.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• ETRI Journal
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• v.32 no.4
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• pp.530-539
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• 2010

As the conventional silicon metal-oxide-semiconductor field-effect transistor (MOSFET) approaches its scaling limits, quantum mechanical effects are expected to become more and more important. Accurate quantum transport simulators are required to explore the essential device physics as a design aid. However, because of the complexity of the analysis, it has been necessary to simulate the quantum mechanical model with high speed and accuracy. In this paper, the modeling of double gate MOSFET based on an adaptive neuro-fuzzy inference system (ANFIS) is presented. The ANFIS model reduces the computational time while keeping the accuracy of physics-based models, like non-equilibrium Green's function formalism. Finally, we import the ANFIS model into the circuit simulator software as a subcircuit. The results show that the compact model based on ANFIS is an efficient tool for the simulation of nanoscale circuits.