• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1257-1268
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• 1993

본 논문에서는 퍼지보상(compensative) 연산자를 이용하는 정보융합(information fusion) 방법을 제안하였다. 제안된 정보융합 방법에서는 보상적인 성질을 갖는 퍼지 총체화(aggregation) 연산자를 역오류전파(back-propagation)신경회로망의 활성화함수(activation function)로 간주하고, 이들 연산자에 수반된 파라메터들을 학습에 의해 결정한다. 결정된 연산자의 파라메터들은 학습자료에 나타난 의사 결정에 수반된 보상도를 표현할 수 있으며, 평가에 불필요한 정보원을 제거하는 성질도 가지고 있다. 제안된 정보융합 구조는 평가지수(sub-criterion)들의 만족도를 입력으로 학습에 의해 결정된 보상연산자에 의해 총체화된 만족도를 제공한다. 제안된 방법은 패턴 인식 문제와 칼라영상의 분할과 인식등 컴퓨터비죤 문제에 적용하여 그 정당성을 입증하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.538-544
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• 1999

In this paper, we propose a new fault detection algorithm for transmission line protection using ANFIS(Adaptive Network Fuzzy Inference System). The developed system consists of two subsystems: fault type classification, and fault location estimation. We use rms value, zero sequence component and positive sequence of current, and then using learning method of neural network, premise and consequent parameters are tuned properly. To prove the performance of the proposcd system, generated data by EMTP(Electr0- Magnetic Transient Program) sin~ulationi s used. It is shown that the proposed relaying classifies fault types accurately and advances fault location estimation.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.586-591
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• 2014

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

• Journal of Bio-Environment Control
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• v.4 no.2
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• pp.246-252
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• 1995

작물, 가축, 농산물을 학문의 대상으로 하는 농학은 기상, 토양 등과 같은 자연 현상으로부터 필요한 데이터를 획득하여 이용한다. 그러나, 이들 데이터는 많은 환경 요인의 영향을 받아 그 거동이 매우 복잡한 비선형적 현상을 나타내는 것이 대부분이다. 따라서, 실험을 통해 획득된 데이터의 처리 및 모형화 등을 위해 기존의 수학적, 통계적 방법을 이용하는 경우에 많은 어려움을 겪게 된다. 이에 최근에는 신경회로망 및 퍼지 이론 등과 같은 인공 지능 기법을 이용하여 이러한 문제점을 해결하기 위한 연구가 활발히 진행되고 있다. 본 강좌에서는 복잡한 비선형 특성 특히 임의적 거동을 보이는 자연 현상을 기술하기 위해 최근에 대두되고 있는 혼돈 이론에 대한 소개를 하고자 한다.(중략)

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.718-723
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• 1993

In this paper, we propose a neural network with fuzzy preprocessor not only for improving the classification accuracy but also for being able to classify objects whose attribute values do not have clear boundaries. The fuzzy input signal representation scheme is included as a preprocessing module. It transforms imprecise input in linguistic form and precisely stated numerical input into multidimensional numerical values. The transformed input is processed in the postprocessing module. The experimental results indicate the superiority of the backpropagation network with fuzzy preprocessor in comparison to the conventional backpropagation network.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2203-2205
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• 1998

This paper describes fuzzy radial basis function networks(FRBFN) extracting fuzzy rules through the learning from training data set. The proposed FRBFN is derived from the functional equivalence between RBF networks and fuzzy inference systems. The FRBFN learn by assigning new fuzzy rules and updating the parameters of existing fuzzy rules. The parameters of the FRBFN are adjusted using the standard LMS algorithm. The performance of the FRBFN is illustrated with function approximation and system identification.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2959-2961
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• 1999

A position control algorithm for a inverted pendulum is studied. The proposed algorithm is based on a fuzzy theory and Generalized Radial Basis Function(GRBF). The conventional fuzzy methods need expert's knowledges or human experiences. The GRBF, which is an optimization algorithm, tunes automatically the input-output membership parameters and fuzzy rules. The simulation is presented to illustrate the approaches.

• Transactions of the Society of Information Storage Systems
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• v.6 no.1
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• pp.18-24
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• 2010

The performance of the speech recognition mainly depends on uncertain factors such as speaker's conditions and environmental effects. The present study deals with the speech recognition of a number of multi-syllable isolated Korean words using soft computing techniques such as back-propagation neural network, fuzzy inference system, and fuzzy neural network. Feature patterns for the speech recognition are analyzed with 12th order thirty frames that are normalized by the linear predictive coding and Cepstrums. Using four models of speech recognizer, actual experiments for both single-speakers and multiple-speakers are conducted. Through this study, the recognizers of combined fuzzy logic and back-propagation neural network and fuzzy neural network show the better performance in identifying the speech recognition.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1952-1953
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• 2011

본 논문에서는 다항식 기반 Radial Basis Function(RBF)신경회로망(Polynomial based Radial Basis function Neural Network)을 설계하고 이를 n-클래스 패턴 분류 문제에 적용한다. 제안된 다항식기반 RBF 신경회로망은 입력층, 은닉층, 출력층으로 이루어진다. 입력층은 입력 벡터의 값들을 은닉층으로 전달하는 기능을 수행하고 은닉층과 출력층사이의 연결가중치는 상수, 선형식 또는 이차식으로 이루어지며 경사 하강법에 의해 학습된다. Networks의 최종 출력은 연결가중치와 은닉층 출력의 곱에 의해 퍼지추론의 결과로서 얻어진다. 패턴분류기의 최적화는 PSO(Particle Swarm Optimization)알고리즘을 통해 이루어진다. 그리고 제안된 패턴분류기는 실제 얼굴인식 시스템으로 응용하여 직접 CCD 카메라로부터 입력받은 데이터를 영상 보정, 얼굴 검출, 특징 추출 등과 같은 처리 과정을 포함하여 서로 다른 등록인물의 n-클래스 분류 문제에 적용 및 평가되어 분류기로써의 성능을 분석해본다.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space