• Journal of the Korean Statistical Society
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• 제29권1호
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• pp.95-108
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• 2000

Consider the oblique factor model X=Af＋$\varepsilon$, with defining relation $\Sigma$=Λ$\Phi$Λ'＋Ψ. This paper is concerned with suggesting an optimal Bayes criterion for determining the number of factors in the model, i.e. dimension of the vector f. The use of marginal likelihood as a method for calculating posterior probability of each model with given dimension is developed under a generalized conjugate prior. Then based on an appropriate loss function, a Bayes rule is developed by use of the posterior probabilities. It is shown that the approach is straightforward to specify distributionally and to imploement computationally, with output readily adopted for constructing required cirterion.

• 전자공학회논문지B
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• 제31B권6호
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• pp.43-52
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• 1994

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권1호
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• pp.1-7
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• 2014

This paper presents a novel method for simultaneously and automatically choosing the nonlinear structures of regressors or discriminant functions, as well as the number of terms to include in a rule-based regression model or pattern classifier. Variables are first partitioned into subsets each of which has a linguistic term (called a causal condition) associated with it; fuzzy sets are used to model the terms. Candidate interconnections (causal combinations) of either a term or its complement are formed, where the connecting word is AND which is modeled using the minimum operation. The data establishes which of the candidate causal combinations survive. A novel theoretical result leads to an exponential speedup in establishing this.

• 산업경영시스템학회지
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• 제40권4호
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• pp.112-119
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• 2017

Selecting suppliers in the global supply chain is the very difficult and complicated decision making problem particularly due to the various types of supply risk in addition to the uncertain performance of the potential suppliers. This paper proposes a multi-phase decision making model for supplier selection under supply risks in global supply chains. In the first phase, the model suggests supplier selection solutions suitable to a given condition of decision making using a rule-based expert system. The expert system consists of a knowledge base of supplier selection solutions and an "if-then" rule-based inference engine. The knowledge base contains information about options and their consistency for seven characteristics of 20 supplier selection solutions chosen from articles published in SCIE journals since 2010. In the second phase, the model computes the potential suppliers' general performance indices using a technique for order preference by similarity to ideal solution (TOPSIS) based on their scores obtained by applying the suggested solutions. In the third phase, the model computes their risk indices using a TOPSIS based on their historical and predicted scores obtained by applying a risk evaluation algorithm. The evaluation algorithm deals with seven types of supply risk that significantly affect supplier's performance and eventually influence buyer's production plan. In the fourth phase, the model selects Pareto optimal suppliers based on their general performance and risk indices. An example demonstrates the implementation of the proposed model. The proposed model provides supply chain managers with a practical tool to effectively select best suppliers while considering supply risks as well as the general performance.

• 전력전자학회논문지
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• 제4권1호
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• pp.1-12
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• 1999

파라미터 변동이나 외란에 강인한 영구자석 동기전동기의 궤환선형화 속도제어기를 설계하고 DSP를 이용하여 실험 시스템을 구현하였다. 시스템의 상태변수에 비하여 매우 느리게 변화하는 파라미터의 추정을 위하여 MRAS를 이용한 추정방법이 MIT rule을 이용하여 유도되었다. 외란이나 시스템의 상태변수 정도의 변화를 보이는 피라미터에 대하여는 그영향이 고려된 준-선형화 비간섭 모델이 유도되었다. 이 모델을 이용하여 제어시스템의 강인성을 얻고자 경계층을 가지는 Sliding mode 제어기를 설계하고 PD 제어기를 적용한 기존의 제어기와 비교하였다. 제안된 제어 방법의 유용성은 Simulation과 DSP에 기반한 실험 시스템을 통하여 검증하였다.

• 대한전자공학회논문지
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• 제27권1호
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• pp.143-150
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• 1990

In this paper, a rule-based method for improving the intelligibility of synthetic speech is proposed. A 12-pole linear prediction coding method is used to model syllable speech signals. A syllable concatenation rule for pause and frame rejection between syllables is developed to improve the naturalness of the synthetic speech. In addition, phonoligical structure transform rule and prosody rule are applied to the synthetic speech by LPC. The illustrative results demonstrate that the synthetic speech obtained by applying these rules has better naturalness than the synthetic speech by LPC.

• 제어로봇시스템학회논문지
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• 제8권12호
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• pp.1056-1060
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• 2002

Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.663-667
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• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

• 한국지반공학회지:지반
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• 제12권2호
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• pp.95-106
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• 1996

본 연구에서는 이방성 항복경계면 이론을 토대로 하여 자연상태의 점성토가 갖는 이방적 특성과 소성적 거동을 고려한 구성모델을 개발하였다. 이 모델은 개선된 이방항복경계면 함수와 새로운 소성포텐셜 함수를 이용한 비관련 유동법칙, 이방경화법칙과 항복면 내의 소성 거동 예측을 위한 새로운 투영법칙 등의 개념을 통하여 개발하였다. 개발된 모델의 검증을 위하여 불교란 점성토에 대한 Ko 압밀과 삼축전단 시험결과들을 비교분석한 결과 본 모델은 점성토의 거동을 잘 예측하는 것으로 나타났다.

• Structural Engineering and Mechanics
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• 제43권2호
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• pp.163-177
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• 2012

This paper presents a LMI(linear matrix inequality)-based fuzzy approach of modeling and active vibration control of geometrically nonlinear flexible plates with piezoelectric materials as actuators and sensors. The large-amplitude vibration characteristics and dynamic partial differential equation of a piezoelectric flexible rectangular thin plate structure are obtained by using generalized Fourier series and numerical integral. Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear structural system, which combines the fuzzy inference rule with the local linear state space model. A robust fuzzy dynamic output feedback control law based on the T-S fuzzy model is designed by the parallel distributed compensation (PDC) technique, and stability analysis and disturbance rejection problems are guaranteed by LMI method. The simulation result shows that the fuzzy dynamic output feedback controller based on a two-rule T-S fuzzy model performs well, and the vibration of plate structure with geometrical nonlinearity is suppressed, which is less complex in computation and can be practically implemented.