• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Industrial Technology
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• v.17
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• pp.323-330
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• 1997

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure, and the mountain clustering technique is used in partitioning of system. TSK rule structure is adopted to form the fuzzy rules, and Back propagation algorithm is used for learning parameters in consequent parts of the rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators. Computer simulations are performed to verify the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.891-899
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• 2015

This paper presents the robust stability condition of uncertain Takagi-Sugeno(T-S) fuzzy systems with time-varying delay. New augmented Lyapunov-Krasovskii function is constructed to ensure that the system with time-varying delay is globally asymptotically stable. Also, less conservative delay-dependent stability criteria are obtained by employing some integral inequality, reciprocally convex approach and new delay-partitioning method. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.425-427
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• 1997

In this paper, a new approach to modeling and control of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure. The mountain clustering technique is used in partitioning of system, and TSK rule structure is adopted to form the fuzzy rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.1-4
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• 2000

본 논문에서는 패턴 데이터(pattern data) 의 분할(partitioning)위하여, 계산량의 단축할 수 있는 효과적인 퍼지 클러스터링 알고리즘(fuzzy clustering algorithm)을 제시한다. 본 논문에 제시된 알고리즘은 두 단계로 수행된다. 첫번째 단계는, 개선된 FCM(Fuzzy C-means)방법에 의해 입력 패턴틀에 대해, 단지 두 번의 반복 수행과정만을 거쳐, 충분히 많은 개수의 초기 클러스터 중 심(center)를 결정한다. 다음 단계에서는, 본 논문에 제시될 클러스터 합치기 알고리즘(cluster merging algorithm)을 통해 각 클러스터의 부피(volume)에 따라 클러스터들을 합치는 과정(merging process)을 하게 된다. 결과적으로 일정한 제한된 개수의 무정형(amorphous)의 클러스터틀의 효과적으로 표현될 수 있다. 본 논문의 마지막에 제시될 실험 결과들은 제시된 방법의 유용성을 보여줄 것이다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2072-2078
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• 2011

In photovoltaic(PV) system, for obtaining maximum efficiency of solar power systems, the solar tracking system must be controlled to match position of the sun. In this paper, we design the solar tracking system to track movement of the sun using CdS sensor modules and to determine direction of the sun under shadow of directions. In addition, for an intelligent computation in tracking of the sun, a fuzzy controller is allocated to space avaliable for splitting area of fuzzy part for the fuzzy input space(grid-type fuzzy partition) in which a fuzzy grid partition divides fuzzy rules bases. As well, a simple model of solar tracking system is designed by two-axis motor control systems and the 8-direction sensor module that can measure shadow from CdS sensor modules by matching of axis of CdS modules and PV panels. We demonstrate this systems is effective for fixed location and moving vessels and our fuzzy controller can track the satisfactorily.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.118-121
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• 1996

This paper describes ECG signal labeling based on Fuzzy clustering, which is necessary at automated ECG diagnosis. The NPPA(Non parametric partitioning algorithm) compares the correlations of wave forms, which tends to recognize the same wave forms as different when the wave forms have a little morphological variation. We propose to apply Fuzzy clustering to ECG QRS Complex labeling, which prevents the errors to mistake by using If-then comparision. The process is divided into two parts. The first part is a parameters extraction process from ECG signal, which is composed of filtering, QRS detection by mapping to a phase space by time delay coordinates and generation of characteristic vectors. The second is fuzzy clustering by FCM(Fuzzy c-means), which is composed of a clustering, an assessment of cluster validity and labeling.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2818-2820
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• 2002

In this paper, we design neurofuzzy networks architecture by means of linear fuzzy inference. The proposed neurofuzzy networks are equivalent to linear fuzzy rules, and the structure of these networks is composed of two main substructures, namely premise part and consequence part. The premise part of neurofuzzy networks use fuzzy space partitioning in terms of all variables for considering correlation between input variables. The consequence part is networks constituted as first-order linear form. The consequence part of neurofuzzy networks in general structure(for instance ANFIS networks) consists of nodes with a function that is a linear combination of input variables. But that of the proposed neurofuzzy networks consists of not nodes but networks that are constructed by connection weight and itself correspond to a linear combination of input variables functionally. The connection weights in consequence part are learned by back-propagation algorithm. For the evaluation of proposed neurofuzzy networks. The experimental results include a well-known NASA dataset concerning software cost estimation.