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퍼지 GMDH 알고리즘과 폐수처리 공정 시스템에의 응용
안태천,노석범,황형수,오성권 圓光大學校 1996 論文集 Vol.31 No.2
본 논문에서는 GMDH(Group Method of Data Handling) 알고리즘을 이용하여 퍼지 모델의 구조와 파라미터를 설정하는 알고리즘을 제안하였다. 퍼지 함의 규칙의 전건부 구조와 파라미터를 동정하기 위해 GMDH 알고리즘과 퍼지 추론을 사용하였고 최적의 후건부 파라미터를 동정하기 위해 최소 자승법을 사용하였다. 제안된 모델링 방법의 성능을 평가하기 위해서 가스로 시계열 데이터와 하수처리 데이터를 사용하였다. 제안된 방법을 사용하면 다른 모델에 비해 우수한 성능을 가진 지능 모델을 얻을수 있다. The Proposed fuzzy modeling implements system structure and parameter identification using the GMDH(Group Method of Data Handling) Algorithm. In this method, We use the GMDH algorithm and fuzzy inference to identify the premise structure and parameter of fuzzy implication rules and least square method to identify optimum consequence parameter. Time seris data for gas furnace and wastewatertreatment data are used for purpose of evaluating the performance of the proposed fuzzy.
Fuzzy Learning Vector Quantization based on Fuzzy k-Nearest Neighbor Prototypes
Seok-Beom Roh,Ji-Won Jeong,Tae-Chon Ahn 한국지능시스템학회 2011 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.11 No.2
In this paper, a new competition strategy for learning vector quantization is proposed. The simple competitive strategy used for learning vector quantization moves the winning prototype which is the closest to the newly given data pattern. We propose a new learning strategy based on k-nearest neighbor prototypes as the winning prototypes. The selection of several prototypes as the winning prototypes guarantees that the updating process occurs more frequently. The design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the proposed learning strategy.
Seok-Beom Roh,Sung-Kwun Oh 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.6
In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the input space with the mechanism of supervision implied by the distribution of data present in the output space. However, like other clustering methods, c-FCM focuses on the distribution of the data. In this paper, we introduce a new method, which by making use of the ambiguity index focuses on the boundaries of the clusters whose determination is essential to the quality of the ensuing classification procedures. The introduced design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the fuzzy classifiers and quantify several essentials design aspects.
Lazy Learning for Nonparametric Locally Weighted Regression
Seok-Beom Roh,Yong Soo Kim,Tae-Chon Ahn 한국지능시스템학회 2020 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.20 No.2
In this study, a newly designed local model called locally weighted regression model is proposed for the regression problem. This model predicts the output for a newly submitted data point. In general, the local regression model focuses on an area of the input space specified by a certain kernel function (Gaussian function, in particular). The local area is defined as a region enclosed by a neighborhood of the given query point. The weights assigned to the local area are determined by the related entries of the partition matrix originating from the fuzzy C-means method. The local regression model related to the local area is constructed using a weighted estimation technique. The model exploits the concept of the nearest neighbor, and constructs the weighted least square estimation once a new query is provided given. We validate the modeling ability of the overall model based on several numeric experiments.
Roh, Seok-Beom,Oh, Sung-Kwun The Korean Institute of Electrical Engineers 2016 Journal of Electrical Engineering & Technology Vol.11 No.6
The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.
Seok-Beom Roh(노석범),Tae-Chon Ahn(안태천),Sung-Kwun Oh(오성권) 한국지능시스템학회 2005 한국지능시스템학회 학술발표 논문집 Vol.15 No.1
In this paper, we propose a new fuzzy set-based polynomial neuron (FSPN) involving the information granule, and new fuzzy-neural networks - Fuzzy Set based Polynomial Neural Networks (FSPNN). We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that expanded from Group Method of Data Handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FSPNN fixed by aid of genetic optimization that has search capability to find the optimal solution on the solution space. We have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model (node) composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules.
Seok-Beom Roh(노석범),Sung-Kwun Oh(오성권),Tae-Chon Ahn(안태천) 한국지능시스템학회 2005 한국지능시스템학회 학술발표 논문집 Vol.15 No.2
We introduce a new architecture of hetero-hybridized feed-forward neural networks composed of fuzzy set-based polynomial neural networks (FSPNN) and polynomial neural networks (PNN) that are based on a genetically optimized multi-layer perceptron and develop their comprehensive design methodology involving mechanisms of genetic optimization and Information Granulation. The construction of Information Granulation based HFSPNN (IG-HFSPNN) exploits fundamental technologies of Computational Intelligence(CI), namely fuzzy sets, neural networks, and genetic algorithms(GAs) and Information Granulation. The architecture of the resulting genetically optimized Information Granulation based HFSPNN (namely IG-gHFSPNN) results from a synergistic usage of the hybrid system generated by combining new fuzzy set based polynomial neurons (FPNs)-based Fuzzy Neural Networks(FNN) with polynomial neurons (PNs)-based Polynomial Neural Networks(PNN). The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being tuned by using Genetic Algorithms throughout the overall development process. However, the new proposed IG-HFSPNN adopts a new method called as Information Granulation to deal with Information Granules which are included in the real system, and a new type of fuzzy polynomial neuron called as fuzzy set based polynomial neuron. The performance of the IG-gHFPNN is quantified through experimentation.