• 제목/요약/키워드: Back-propagation network

검색결과 1,104건 처리시간 0.044초

인공신경망을 이용한 PHC 매입말뚝의 지지력 평가 (Evaluation of Bearing Capacity on PHC Auger-Drilled Piles Using Artificial Neural Network)

  • 이송;장주원
    • 한국구조물진단유지관리공학회 논문집
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    • 제10권6호
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    • pp.213-223
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    • 2006
  • 본 연구에서는 화강풍화토 지반에 시공된 PHC 매입말뚝의 지지력의 평가를 위해 인공신경망을 적용하였다. 오류역전파 인공신경망의 적용성을 증명하기 위해 168개의 PHC 매입말뚝의 현장시험 데이터가 사용되었다. 연구결과 오류역전파 인공신경망의 말뚝지지력 평가가 동재하시험결과와 잘 일치함을 보여주었으며, 이러한 결과는 인공신경망을 이용한 PHC 매입말뚝의 지지력 평가가 신뢰성이 있음을 보여준다.

한글문서분류에 SVD를 이용한 BPNN 알고리즘 (BPNN Algorithm with SVD Technique for Korean Document categorization)

  • 리청화;변동률;박순철
    • 한국산업정보학회논문지
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    • 제15권2호
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    • pp.49-57
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    • 2010
  • 본 논문에서는 역전파 신경망 알고리즘(BPNN: Back Propagation Neural Network)과 Singular Value Decomposition(SVD)를 이용하는 한글 문서 분류 시스템을 제안한다. BPNN은 학습을 통하여 만들어진 네트워크를 이용하여 문서분류를 수행한다. 이 방법의 어려움은 분류기에 입력되는 특징 공간이 너무 크다는 것이다. SVD를 이용하면 고차원의 벡터를 저차원으로 줄일 수 있고, 또한 의미있는 벡터 공간을 만들어 단어 사이의 중요한 관계성을 구축할 수 있다. 본 논문에서 제안한 BPNN의 성능 평가를 위하여 한국일보-2000/한국일보-40075 문서범주화 실험문서집합의 데이터 셋을 이용하였다. 실험결과를 통하여 BPNN과 SVD를 사용한 시스템이 한글 문서 분류에 탁월한 성능을 가지는 것을 보여준다.

SPMSM 드라이브의 속도 센서리스를 위한 하이브리드 지능제어 (Hybrid Intelligent Control for Speed Sensorless of SPMSM Drive)

  • 이정철;이홍균;정동화
    • 대한전기학회논문지:시스템및제어부문D
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    • 제53권10호
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    • pp.690-696
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    • 2004
  • This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

신경회로망을 이용한 유도전동기 속도제어 (The speed control of induction motor using neural networks)

  • 김세찬;원충연
    • 대한전기학회논문지
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    • 제45권1호
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    • pp.42-53
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    • 1996
  • The paper presents a speed control system of vector controlled induct- ion motor using neural networks. The main feature of proposed speed control system is a Neural Network Controller(NNC) which supplies torque current to induction motor and Neural Network Emulator(NNE) which captures the forward dynamics of induction motor. A back propagation training algorithm is employed to train the NNE and NNC. In order to determine the NNC output error, plant(induction motor) output error can be back propagated through the NNE. The NNC and NNE for speed control of vector controlled induction motor is carried out by TMS320C30 DSP and IGBT current regulated PWM inverter. Through computer simulation and experimental results, it is verified that proposed speed control system is robust to the load variation. (author). refs., figs.

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신경회로망을 이용한 비선형 플랜트의 적응제어 (Adaptive controls for non-linear plant using neural network)

  • 정대원
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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    • pp.215-218
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    • 1997
  • A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.

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신경회로망을 이용한 종합주가지수의 변화율 예측 (Prediction of Monthly Transition of the Composition Stock Price Index Using Error Back-propagation Method)

  • 노종래;이종호
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1991년도 하계학술대회 논문집
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    • pp.896-899
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    • 1991
  • This paper presents the neural network method to predict the Korea composition stock price index. The error back-propagation method is used to train the multi-layer perceptron network. Ten of the various economic indices of the past 7 Nears are used as train data and the monthly transition of the composition stock price index is represented by five output neurons. Test results of this method using the data of the last 18 months are very encouraging.

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고압 수용가용 배전반의 intelligent화 연구 (A Study on the Intelligent High Voltage Switchboard for Custormer)

  • 변영복;조기연;구헌회
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1994년도 하계학술대회 논문집 A
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    • pp.444-446
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    • 1994
  • This paper describes the design of a digital multifunction controller for the intelligent high voltage customer switchboard and proposes a relaying algorithm for high impedance faults using back-propagation neural network. The hardware design uses the three microprocessors and global memory architecture to achive real time operation and control 4 feeders. The controller uses a 64-point radix-4 DIF FFT algorithm to measure the harmonic and relay parameters. Synthesized fault current waveforms are used to train and test the back - propagation network.

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부분 학습구조의 신경회로와 로보트 역 기구학 해의 응용 (A neural network with local weight learning and its application to inverse kinematic robot solution)

  • 이인숙;오세영
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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    • pp.36-40
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    • 1990
  • Conventional back propagation learning is generally characterized by slow and rather inaccurate learning which makes it difficult to use in control applications. A new multilayer perception architecture and its learning algorithm is proposed that consists of a Kohonen front layer followed by a back propagation network. The Kohonen layer selects a subset of the hidden layer neurons for local tuning. This architecture has been tested on the inverse kinematic solution of robot manipulator while demonstrating its fast and accurate learning capabilities.

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Implementation of Speed Sensorless Induction Motor drives by Fast Learning Neural Network using RLS Approach

  • Kim, Yoon-Ho;Kook, Yoon-Sang
    • 전력전자학회:학술대회논문집
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    • 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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    • pp.293-297
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    • 1998
  • This paper presents a newly developed speed sensorless drive using RLS based on Neural Network Training Algorithm. The proposed algorithm has just the time-varying learning rate, while the wellknown back-propagation algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The theoretical analysis and experimental results to verify the effectiveness of the proposed control strategy are described.

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ANN에 의한 IPMSM의 센서리스 속도제어 (Sensorless Speed Control of IPMSM Drive with ANN-based)

  • 이홍균;이정철;정동화
    • 전기학회논문지P
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    • 제52권4호
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    • pp.154-160
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    • 2003
  • This paper is proposed a ANN-based rotor position and speed estimation method for IPMSM by measuring the currents. Because the proposed estimator treats the estimated motor speed as the weights, it is possible to estimate motor speed to adapt back propagation algorithm with 2 layered neural network. The proposed control algorithm is applied to IPMSM drive system. The operating characteristics controlled by neural networks are examined in detail.