• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.68-77
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• 1990

In this paper, a neural patten recognition method for the automatic circuit diagram reading system is proposed. The proposed procedure to recognize a deformed logic symbols is composed of three stages: feature detection, log mapping, and pattern classification. In the feature detection stage, a modified competitive learning algorithm where each pattern has the inhibition weight as well as the activation weight is developed. The global information of hand-written logic symbols is obtained by the feature detection neural network having both the inhibition and activation weights. The obtained global data is then transformed into a log space by the conformal mapping where according to the Schwartz's theory about the human visual signal process-ing, the degree of rotation and the scale change are mapped into the translation change. Logic symbols are finally classified by a three layer perceptron trained by the error back propagation algorithm. The computer simulation demonstrates that the proposed multistage neural network system can recognize well the deformed patterns of hand-written logic circuit diagrams.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.10
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• pp.1591-1597
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• 1989

Through the matematical alaysis of EX-NOR logic relation between the input vector and the memory matrix, we propose a new method for optical implementation of the bipolar Hopfield neural network model based on the optical vector-matrix multiplier.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2265-2267
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• 1998

This paper deals with study of Fast Speaker Adaptation Type Speech Recognition, and to analyze speech signal efficiently in time domain and time-frequency domain, utilizes SCONN[1] with Speech Signal Process suffices for Fast Speaker Adaptation Type Speech Recognition, and examined Speech Recognition to investigate adaptation of system, which has speech data input after speaker dependent recognition test.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.211-213
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• 1993

In this paper, we proposed a design method of self-learning fuzzy logic controller using fuzzy neural network. The parameters of membership function in premise are modified by descent method and also consequent parameters by learning mechanism of animal conditioning theory. The proposed method is applied to pole balancing system in order to confirm the feasibility.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.33-38
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• 2014

The FCM based hybrid RBF network is a heterogeneous learning network model that applies FCM algorithm between input and middle layer and applies Max_Min algorithm between middle layer and output. The Max-Min neural network uses winner nodes of the middle layer as input but shows inefficient learning in performance when the input vector consists of too many patterns. To overcome this problem, we propose a dynamic learning rate control based on fuzzy logic. The proposed method first classifies accurate/inaccurate class with respect to the difference between target value and output value with threshold and then fuzzy membership function and fuzzy decision logic is designed to control the learning rate dynamically. We apply this proposed RBF network to the character recognition problem and the efficacy of the proposed method is verified in the experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.390-395
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• 2004

A semiconductor manufacturing process is the complicate and dynamic process, and consists of many sub-processes. An etching process is the most important process in the semiconductor fabrication. In this paper, the decision support system based upon data mining and knowledge discovery is an important factor to improve the productivity and yield. The proposed decision support system consists of a neural network model and an inference system based on fuzzy logic Firstly, the product results are predicted by the neural network model constructed by the product patterns that represent the quality of the etching process. And the product patters are classified by expert's knowledge. Finally, the product conditions are estimated by the fuzzy inference system using the rules extracted from the classified patterns. Prediction of product qualities can be linked to each input and process variables. We employ data mining and intelligent techniques to find the best condition of the etching process. The proposed decision support system is efficient and easy to be implemented for the process management based upon expert's knowledge.

• Journal of Korea Multimedia Society
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• v.2 no.4
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• pp.490-495
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• 1999

In this paper, a method of improving the learning time and convergence rate is proposed to exploit the advantages of artificial neural networks and fuzzy theory to neuron structure. This method is applied to the XOR Problem, n bit parity problem which is used as the benchmark in neural network structure, and recognition of digit image in the vehicle plate image for practical image application. As a result of the experiments, it does not always guarantee the convergence. However, the network showed improved the teaming time and has the high convergence rate. The proposed network can be extended to an arbitrary layer Though a single layer structure Is considered, the proposed method has a capability of high speed 3earning even on large images.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.431-434
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• 2001

단층 퍼셉트론이 처음 개발되었을 때, 간단한 패턴을 인식하는 학습 기능을 가지고 있기 장점 때문에 학자들의 관심을 끌었다. 단층 퍼셉트론은 한 개의 소자를 이용해서 이진 논리를 가중치(weight)의 변경만으로 모두 표현할 수 있는 장점 때문에 영상처리, 패턴인식, 장면인식 등에 이용되어 왔다. 최근에, 역전파학습(Back-Propagation Learning)알고리즘이 이진 공간내의 매핑 문제에 적용되고 있다. 그러나, 역전파 학습알고리즘은 연속공간 내에서 긴 학습시간과 비효율적인 수행의 문제를 가지고 있다. 일반적으로 역전파 학습 알고리즘은 간단한 이진 공간에서 매핑하기 위해서 많은 반복과정을 요구한다. 역전파 학습 알고리즘에서는 은닉층의 뉴런의 수는 주어진 문제를 해결하기 위해서 우선순위(prior)를 알지 못하기 때문에 입력층과 출력층내의 뉴런의 수에 의존한다. 따라서, 3층 신경회로망의 적용에 있어 가장 중요한 문제중의 하나는 은닉층내의 필요한 뉴런수를 결정하는 것이고, 회로망 합성과 가중치 결정에 대한 적절한 방법을 찾지 못해 실제로 그 사용 영역이 한정되어 있었다. 본 논문에서는 패턴 분류를 위한 새로운 학습방법을 제시한다. 훈련입력의 기하학적인 분석에 기반을 둔 이진 신경회로망내의 은닉층내의 뉴런의 수를 자동적으로 결정할 수 있는 NETLA(Newly Expand and Truncate Learning Algorithm)라 불리우는 기하학적 학습알고리즘을 제시하고, 시뮬레이션을 통하여, 제안한 알고리즘의 우수성을 증명한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.672-674
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• 2023

본 논문은 뉴로-심볼릭 구조를 바탕으로 일반 텍스트로부터 온톨로지 생성이 가능한 심층 신경망 기반 온톨로지 추출기를 제안한다. 온톨로지 추출 단계를 (i) 온톨로지 학습 및 (ii) 온톨로지 생성의 2 단계로 상정, (i) 일반 텍스트로부터 문장 구조 및 논리적 관계를 학습하는 트랜스포머 기반 심층 생성 신경망 출력을 이용하여 (ii) 계층적으로 결합한 심볼릭 추론기로 온톨로지를 생성하는 뉴로-심볼릭 구조 온톨로지 추출기를 구현하였다. 1800 개 훈련 집합으로 학습 후 200 개 테스트 집합으로 평가한 결과, 정확도 91.9%, Precision 100%, Recall 99.1%로 비교 모델 OpenIE 의 성능에 비해서 각각 83.8%, 1.8%, 3.5% 개선된 것을 확인하였다. 정성적 품질에 있어서, 복잡한 문장 (예: 관계대명사, 접속사, 중첩 구조)에서도 비교 모델에 비해 더 정밀한 온톨로지 생성 결과를 보였다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.49-57
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• 1999

The fusion of fuzzy set theory and neural networks technologies have concentrated on applying neural networks to obtain the optimal rule bases of fuzzy logic system. Unfortunately, this is very hard to achieve due to limited learning capabilities of neural networks. To overcome this difficulty, we propose a new approach in which rough set theory and neuro-fuzzy fusion are combined to obtain the optimal rule base from input/output data. Compared with conventional FNN, the proposed algorithm is considerably more realistic because it reduces overlapped data when construction a rule base. This results are applied to the construction of inference rules for controlling the temperature at specified points in a refrigerator.