• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.128-135
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• 2015

In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.69-76
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• 2008

This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.146-148
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• 2005

In this paper, we propose a genetically optimized identification of information granulation(IG)-based fuzzy model. To optimally design the IG-based fuzzy model we exploit a hybrid identification through genetic alrogithms(GAs) and Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the seleced input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the inital parameters are tuned effectively with the aid of the genetic algorithms and the least square method. And also, we exploite consecutive identification of fuzzy model in case of identification of structure and parameters. Numerical example is included to evaluate the performance of the proposed model.

• International journal of advanced smart convergence
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• v.2 no.1
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• pp.12-17
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• 2013

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the parameters of the consequence part are estimated by least square errors. The proposed model is evaluated with the performance using the data widely used in nonlinear process. Finally, this paper shows that the proposed model has the good result for high-dimension nonlinear process.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.550-552
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• 2002

플러스터링이란 주어진 데이터 집합의 패턴들을 비슷한 성실을 가지는 그룹으로 나누어 패턴 상호간의 관계를 정립하기 위한 방법론으로, 지금가지 이를 위한 많은 알고리즘들이 개발되어 왔으며, 패턴인식, 영상 처리 등의 여러 공학 분야에 널리 적용되고 있다. FCM(Fuzzy C-Means) 알고리즘은 최소자승 기준함수(least square criterion function)에 퍼지이론을 적용만 목적함수의 반복최적화(iterative optimization)에 기반을 둔 방식으로, 하드 분할에 의한 기존의 클러스터링 방법이 승자(winner take all) 형태의 방법론을 취하는데 비하여, 각 패턴이 특정 클러스터에 속하는 소속정도를 줌으로써 보다 정확한 정보를 형성하도록 도와준다. 본 논문에서는 FCM 기법을 이용한 웹로그 분석을 하고자 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.233-236
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• 2001

In this paper, we propose a TSK-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy C-Means) clustering and hybrid GA(genetic algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive Genetic Algorithm) and RLSE(Recursive Least Square Estimate). we applied the proposed method to Iris data classification problems and obtained a better performance than previous works.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1140-1141
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• 2007

A method of modeling the Smart Power IC is presented in this paper, which is based on the IC's typical operation characteristics and small signal frequency response data. Using the least square identification, the IC's dynamic mathematical model, which is expressed as transfer function, can be synthesized from the experimentally obtained gain and phase data. The practicability and effectiveness of the method are verified by means of experiments.