• 한국음향학회지
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• 제14권6호
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• pp.54-61
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• 1995

본 논문에서는 총 106개의 단어로 구성되는 30개의 한국어 속담 문장에 대해 프레임 단위로 인식하는 분산 신경망을 구현하였다. 음성에 대한 특징값으로는 PLP 켐스트럼과 에너지 및 영교차율을 사용하였으며 분산 신경망의 입력으로 사용되는 이 특징값들이 음성의 시간적 특성을 잘 반영할 수 있도록 한 프레임 주변의 넓은 영역에 걸쳐 데이타를 수집하였다. 20대 젊은 남자가 30개의 속담을 5번씩 발음하였다. 신경망 학습에 네집단을 사용하고 학습에 참여하지 않은 나머지 한집단은 인식용으로 사용하였다. 속담내의 단어와 단어 사이는 구별이 잘 되도록 묵음 구간을 두어 발음하였다. 인식 결과음소를 판 군별로 분류하는 대분류 신경망에서의 각 군의 프레임 인식율은 네 집단을 학습에 사용한 경우 $95.3\%$를 나타내었다.

• ETRI Journal
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• 제13권2호
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• pp.34-41
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• 1991

In this paper, a new distributed parallel algorithm for pattern classification based upon Self-Organizing Neural Network(SONN)[10-12] is developed. This system works without any information about the number of clusters or cluster centers. The SONN model showed good performance for finding classification information, cluster centers, the number of salient clusters and membership information. It took a considerable amount of time in the sequential version if the input data set size is very large. Therefore, design of parallel algorithm is desirous. A new distributed parallel algorithm is developed and experimental results are presented.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
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• pp.39-40
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경의 분산 게이트웨이 시스템(Distributed-gateway System)에서 하위의 수 천 개 이상의 센서로부터 데이터를 전송받는 각 게이트웨이의 작업부하(Workload)를 감소시키고 데이터 처리 속도를 향상시키기 위하여 신경망(Neural network) 알고리즘을 이용한 센서 데이터 할당 기법을 소개한다. 각 센서의 중요도에 따른 Weight와 측정 간격에 따른 Bias를 설정하고 학습과정을 통해 Output weight를 산출하여 데이터를 효율적으로 게이트웨이에 할당시킴으로써 신뢰성과 정확성, 신속성을 확보한다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 춘계학술대회 논문집
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• pp.125-130
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• 1997

In this paper, it is presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C3x is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, andsuitable for implementation of robust control.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• 대한전기학회논문지
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• 제41권4호
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• pp.331-338
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• 1992

The application of Connectionist expert system using neural network to fault diagnosis of power system is presented and compared with rule-based expert system. Also, the merits of Connectionist model using neural network is presented. In this paper, the neural network for fault diagnosis is hierarchically composed by 3 neural network classes. The whole power system is divided into subsystems, the neural networks (Class II) which take charge of each subsystem and the neural network (Class III) which connects subsystems are composed. Every section of power system is classified into one of the typical sections which can be applied with same diagnosis rules, as line-section, bus-section, transformer-section. For each typical section, only one neural network (Class I) is composed. As the proposed model has hierarchical structure, the great reduction of learning structure is achieved. With parallel distributed processing, we show the possibility of on-line fault diagnosis.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.65-68
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• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• 물과 미래
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• 제29권4호
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• pp.109-118
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• 1996

신경망이론은 분산기억성질과 병렬국소처리를 수행하는 뇌의 활동을 이론화한 수학모형이다. 이러한 신경망이론의 장점은 분류문제, 대규모로 결합된 최적화문제, 비선형 사상문제 등에서 잘 나타나므로, 이 점을 이용하여 복잡한 강우의 예측을 시도하였다. 신경망이론을 적용하기 위해서 연속적인 값으로 표시되는 입력자료와 출력자료를 학습한 후 강우예측을 시행할 수 있는 다층신경망 모형을 구성하였다. 신경망이론에 의한 강우예측은 서울지역과 소양강유역의 1 시간 대위 강우자료에 적용하였다. 그 결과는 대체로 만족할 만하였다. 따라서 신경망이론은 양질의 자료가 충분히 확보될 경우복잡한 강우현상을 잘 예측할 것으로 기대된다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.759-763
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of Information Processing Systems
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• 제18권6호
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• pp.755-762
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• 2022

The existing electrical engineering design method and the dynamic objective function in the application process of automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design method based on neural network and the application of automatic control system. This paper analyzes the structure and working mechanism of electrical engineering automation control system by an automation control model with main control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerant control is figured out. The automatic control power coefficient is distributed based on an ideal control effect of system. According to the distribution results, an automatic control algorithm is based on neural network for accurate control. The experimental results show that the electrical automation control method based on neural network can significantly reduce the control following error to 3.62%, improve the accuracy of the electrical automation tracking control, thus meeting the actual production needs of electrical engineering automation control system.