• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.79-85
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• 2001

We obtain a solution of inverse kinematic of 3 axis manipulator by using a self-organizing neral network(SONN) with a fuzzy compensator. The self-organizing neural network using the gaussian potential function as the activation function has one hidden layer in the first learning time. The network obtains the optimal number of node by increasing the number of hidden layer node through the learning, and the fuzzy compensator has the optimal loaming rate of neutral network. In this results, we can confirmed that the learning rate is improved and the rapid convergence to the steady-state.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1135-1141
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• 2004

In this study, this system makes use of the analog infrared rays sensor and converts the feature of fish outline when sensor is operating with CPU(80C196KC). Then, after signal processing, this feature is classified a special feature and a outline of fish by using the neural network, one of the artificial intelligence scheme. This neural network classifies fish pattern of very simple and short calculation. This has linear activation function and the error back propagation is used as a teaming algorithm. And the neural network is learned in off-line process. Because an adaptation period of neural network is too long when random initial weights are used, off-line teaming is induced to decrease the progress time.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.395-399
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• 2001

This paper describes modular fuzzy inference systems(MFIS) with adaptive capability to extract fuzzy inference modules from observation data through the learning process. The proposed MFIS is based on the structural similarity to Tagaki-Sugeno fuzzy models and a modular neural architecture. The learning of MFIS is done by assigning new fuzzy inference modules and by updating the parameters of existing modules. The fuzzy inference modules consist of local model network and fuzzy gating network. The parameters of the MFIS are updated by the standard LMS algorithm. The performance of the MFIS is illustrated with adaptive control of a nonlinear dynamic system.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1381-1384
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• 2004

대상 지점의 수질 예측은 단순한 모델로 설명하는데 쉽지 않을 뿐만 아니라 많은 오차를 내포하고 있다. 그러나 최근, 신경회로망, 퍼지 논리, 전문가 시스템 및 유전자 알고리즘과 같은 인공지능이 대두되면서 복잡한 비선형 과정들을 나타낼 수 있게 되었다. 나아가 진정한 인공 지능을 실현하기 위해서는 신경회로망, 퍼지 논리, 전문가 시스템 및 유전자 알고리즘을 보다 효과적으로 이용하고 통합해야 가능할 것으로 기대된다. 본 연구에서는 유전자 알고리즘(Genetic Algorithm)을 T-S 퍼지시스템(Takagj-Sugeno Fuzzy system)의 삼각형 멤버쉽 함수 형태와 규칙 베이스를 최적화하기 위한 도구로 사용하였으면, 예측은 T-S 퍼지 시스템을 이용하여 실시하였다. 대상지점은 영산강 유역의 나주지점을 선정하여 유량자료 및 수질자료를 이용하여 GA와 T-S 퍼지 시스템의 결합에 의해 수질 예측을 실시할 결과 돌연변이율$(P_m)$ $0.05\~0.1$에서 우수한 결과를 얻을 수 있었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.153-157
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• 2004

본 논문은 패턴인식을 위해 사용할 수 있는 감독학습을 이용한 supervised IAFC neural network 1과 supervised IAFC neural network 2를 제안하였다 Supervised IAFC neural network 1과 supervised IAFC neural network 2는 LVQ(Learning Vector Quantization)를 퍼지화한 새로운 퍼지 학습법칙을 사용하고 있다. 이 새로운 퍼지 학습 법칙은 기존의 학습률 대신에 퍼지화된 학습률을 사용하고 있는데, 이 퍼지화된 학습률은 조건 확률을 퍼지화 한 것에 근간을 두고 있다. Supervised IAFC neural network 1과 supervised IAFC neural network 2의 성능과 오류역전파 신경회로망의 성능을 비교하기 위하여 iris 데이터를 사용하였는데, 실험결과 supervised IAFC neural network 2 의 성능이 오류역전파 신경회로망의 성능보다 우수함이 입증되었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.766-776
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• 2001

In the constructing of the multi layer neural network, the network topology is often chosen arbitrarily for different applications, and the optimum topology of the network is determined by the long processing of the trial and error. In this paper, we propose the automatic topology construction using the fuzzy rule that optimizes the neurons of hidden layer, and prune the weights connecting the hidden layer and the output layer during the training process. The simulation of pattern recognition, and the experiment of the mapping of the inverted pendulum showed the effectiveness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.231-238
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• 1993

A rules of fuzzy control is to represent an expert‘s and engineer‘s ambiguous control knowledge of system with some lingustic rules. This rule is very difficult to represent perfectly because expert‘s knowledge is not precise and the rule is not perfect. We propose the fuzzy reasoning and learning to upgrade precision of imperfect rules successively after system running. In the proposed method, the precision of the backward part of a fuzzy rule is improved by back propagation learning method. Also, the method reasons the compatibility degree of the forward part of fuzzy rule by associative memory method. This method this is successfully applied to design auto-parking fuzzy controller in which expert‘s technology and knowledge are required in the limited area.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.3-6
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• 1991

본 연구에서는 음성의 패턴작성법을 개선하고 음성인식율을 향상시키기 위하여 퍼지패턴매칭을 개선한 뉴럴퍼지패턴매칭에(a neural-fuzzy pattern matching)의해 특정화자 고립단어인식을 수행하였다. 이 방법에서는 신경회로망의 연상기억에 의한 사상에 의해 패턴을 작성하여 주파수변동을 흡수하고 표준패턴고 선형매칭에 의해 유사도를 측정하여 인식하므로써 시간변동의 문제를 보완하였다. 또한, 이 방법에서 사용하는 특징피라미터는 2진화 스펙트럽이며, 유사도는 논리연산에 의해 측정되기 때문에 종래의 왜곡척도를 이용한 DTW 방법에 비해 기억용량과 계산량이 매우 작다. 이 방법의 인식성능을 평가하기 위하여 남녀가 발성한 28개의 도시명을 대상으로 인식실험을 수행한 결과, 신경회로망을 이용하지 않은 퍼지패턴매칭보다 오인식을 감소시켰으며, 뉴럴-퍼지 패턴매칭에 의한 특정화자 고립단어인식의 우수성을 확인하였다.

• ICROS
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• v.1 no.3
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• pp.101-108
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• 1995

이 글에서는 퍼지제어기의 기본 구성에 대해 간단히 다루고 모델에 근거해 다음 제어상태를 예견해 내는 제어기법인 모델참조 적응을 기반으로 한 적응 퍼지제어에 대해서, 그리고 신경회로망을 이용한 퍼지제어기의 파라미터의 조정과 클러스터링을 통해서 퍼지규칙을 예측하는 적응 퍼지제어기에 대해서 살펴보았다.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.109-116
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• 2005

This paper is to develop the prototype of a decision making for loan granting system at banks and to evaluate the effectiveness of it. The prototype is called at FENET-LG in this paper. The decision to grant a loan is an unstructured and vagueness task because it is required a tremendous amount of data and many complex relationships among them. Evaluating these many data and relationships is a difficult task even for most experienced decision maker of bank. Therefore, where complex judgement is required, the decision maker of bank may benefit from the use of fuzzy expert network to support the evaluation of ability to pay back. Given the characteristics of decision maker of banking facilities judgement task about ability to pay back, the prototype system named FENET-LG is constructed by integration of fuzzy expert system and neural network. The FENET-LG takes advantage of both the deductive approach of fuzzy expert system and the inductive approach of a neural network to provide a decision aid designed to support and facilitate the process of conducting a judgement of ability to pay back.