• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1647-1651
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• 2009

The uncertainty or imprecision associated with vague parameters and weighting sets, reduces the ability to decide what alternative is better for a particular location. To efficiently reduce the effect of imprecision frequently arising in available information, fuzzy theory has been used to improve consideration of imprecision in a Multi-Criteria Decision Analysis (MCDA) problem. Fuzzy logic offers a way to represent and handle imprecision present in continuous real world applications. A GIS implementing fuzzy set theory, (referred to in this paper as the "Spatial Fuzzy Approach") enables decision makers to express imprecise concepts associated with geographic data and provides decision makers the ability to have even more definition and discrimination in terms of the best alternatives for a particular spatial location. This study is focused on addressing questions pertaining to the methodology of floodplain analysis using GIS and Spatial Fuzzy MCDA to evaluate flood damage reduction alternatives. The issues will be examined in a case study of the Suyoung River Basin in Pusan, Korea.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2005년도 춘계학술대회
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• pp.485-493
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• 2005

Using 1991 FDIC financial statement data, we develop fuzzy clusters of the data set. We also identify the distinctive characteristics of the fuzzy clustering algorithm and compare the closest hard-partitioning result of the fuzzy clustering algorithm with the outcomes of two self-organizing neural networks. When nine clusters are used, our analysis shows that the fuzzy clustering method distinctly groups failed and extreme performance banks from control (healthy) banks. The experimental results also show that the fuzzy clustering method and the self-organizing neural networks are promising tools in identifying potentially failing banks.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.621-632
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• 2013

In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.45-49
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• 1996

In this paper, we will define Choquet integrals of set-valued functions. Then, we show some properties of Choquet integrals of set-valued functions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권2호
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• pp.167-172
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• 2012

In biology the advent of the high-throughput technology for sequencing, probing, or screening has produced huge volume of data which could not be manually handled. Biologists have resorted to software tools in order to effectively handle them. This paper introduces a bioinformatics tool to help biologists find potentially interesting pathway maps from a transcriptome data set in which the expression levels of genes are described for both case and control samples. The tool accepts a transcriptome data set, and then selects and categorizes some of genes into four classes using a fuzzy filtering technique where classes are defined by membership functions. It collects and edits the pathway maps related to those selected genes without analyst' intervention. It invokes a sequence of web service functions from KEGG, which an online pathway database system, in order to retrieve related information, locate pathway maps, and manipulate them. It maintains all retrieved pathway maps in a local database and presents them to the analysts with graphical user interface. The tool has been successfully used in identifying target genes for further analysis in transcriptome study of human cytomegalovirous. The tool is very helpful in that it can considerably save analysts' time and efforts by collecting and presenting the pathway maps that contain some interesting genes, once a transcriptome data set is just given.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2097-2098
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• 2006

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• 전자공학회논문지SC
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• 제39권5호
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• pp.18-25
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• 2002

본 논문에서는 컴퓨터 연산을 통한 언어형 퍼지 제어 시스템의 안정도를 해석하는 새로운 방법을 제안한다. 제안하는 방법은 최근 각광을 받고 있는 선형행렬부등식을 이용한 방법이다. 기존의 수치적 방법과 비교할 때 본 논문에서 제안되는 방식의 특징은 좀더 완화된 방식으로 안정화 문제뿐 아니라 고정점 레귤레이션 문제에도 적용될 수 있는 특징을 가지고 있다. 끝으로 컴퓨터 모의 실험을 통하여 제안한 방법의 타당성을 확인한다.

• Journal of applied mathematics & informatics
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• 제28권3_4호
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• pp.639-649
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• 2010

In this paper, a definition of interval-valued fuzzy regular language (IVFRL) is proposed and their related properties studied. A model of finite automaton (DFA and NDFA) with interval-valued fuzzy transitions is proposed. Acceptance of interval-valued fuzzy regular language by the finite automaton (DFA and NDFA) with interval-valued fuzzy transitions are examined. Moreover, a definition of finite automaton (DFA and NDFA) with interval-valued fuzzy (final) states is proposed. Acceptance of interval-valued fuzzy regular language by the finite automaton (DFA and NDFA) with interval-valued fuzzy (final) states are also discussed. It is observed that, the model finite automaton (DFA and NDFA) with interval-valued fuzzy (final) states is more suitable than the model finite automaton (DFA and NDFA) with interval-valued fuzzy transitions for recognizing the interval-valued fuzzy regular language. In the end, interval-valued fuzzy regular expressions are defined. We can use the proposed interval-valued fuzzy regular expressions in lexical analysis.

• 제어로봇시스템학회논문지
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• 제13권1호
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• pp.46-51
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• 2007

In this paper, we propose a new data clustering method using local probability and hypothesis theory. To cluster the test data set we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set using mean standard deviation and variance etc. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. For evaluating, the proposed classification method is compared to the conventional fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm. The simulation results show more accuracy than results of fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.624-627
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• 2005

This paper presents a new data classification method using the Robbins Monro stochastic approximation algorithm k-nearest neighbor and distribution analysis. To cluster the data set, we decide the centroid of the test data set using k-nearest neighbor algorithm and the local area of data set. To decide each class of the data, the Robbins Monro stochastic approximation algorithm is applied to the decided local area of the data set. To evaluate the performance, the proposed classification method is compared to the conventional fuzzy c-mean method and k-nn algorithm. The simulation results show that the proposed method is more accurate than fuzzy c-mean method, k-nn algorithm and discriminant analysis algorithm.