• 제목/요약/키워드: Back propagation neural network

검색결과 1,073건 처리시간 0.03초

퍼지 신경회로망을 이용한 원격감지 영상의 분류 (Classification of remotely sensed images using fuzzy neural network)

  • 이준재;황석윤;김효성;이재욱;서용수
    • 전자공학회논문지S
    • /
    • 제35S권3호
    • /
    • pp.150-158
    • /
    • 1998
  • This paper describes the classification of remotely sensed image data using fuzzy neural network, whose algorithm was obtained by replacing real numbers used for inputs and outputs in the standard back propagation algorithm with fuzzy numbers. In the proposed method, fuzzy patterns, generated based on the histogram ofeach category for the training data, are put into the fuzzy neural network with real numbers. The results show that the generalization and appoximation are better than that ofthe conventional network in determining the complex boundary of patterns.

  • PDF

Precision Position Control of PMSM using Neural Observer and Parameter Compensator

  • Ko, Jong-Sun;Seo, Young-Ger;Kim, Hyun-Sik
    • Journal of Power Electronics
    • /
    • 제8권4호
    • /
    • pp.354-362
    • /
    • 2008
  • This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

인공신경망을 이용한 평면파괴 안정성 예측 (A Prediction of the Plane Failure Stability Using Artificial Neural Networks)

  • 김방식;이성기;서재영;김광명
    • 한국지반공학회:학술대회논문집
    • /
    • 한국지반공학회 2002년도 가을 학술발표회 논문집
    • /
    • pp.513-520
    • /
    • 2002
  • The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

  • PDF

차량 시뮬레이터 접목을 위한 실시간 인체거동 해석기법 (Real-Time Analysis of Occupant Motion for Vehicle Simulator)

  • 오광석;손권;최경현
    • 대한기계학회논문집A
    • /
    • 제26권5호
    • /
    • pp.969-975
    • /
    • 2002
  • Visual effects are important cues for providing occupants with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant's posture, therefore, the total human body motion must be considered in a graphic simulator. A real-time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and lane-change for acquiring the accelerations of chassis of the vehicle model. A hybrid III 50%ile adult male dummy model was selected and modeled in an ellipsoid model. Multibody system analysis software, MADYMO, was used in the motion analysis of an occupant model in the seated position under the acceleration field of the vehicle model. Acceleration data of the head were collected as inputs to the viewpoint movement. Based on these data, a back-propagation neural network was composed to perform the real-time analysis of occupant motions under specified driving conditions and validated output of the composed neural network with MADYMO result in arbitrary driving scenario.

신경망을 이용한 콘크리트의 압축강도 및 슬럼프값 추정 (The Prediction of Compressive Strength and Slump Value of Concrete Using Neural Networks)

  • 최영화;김종인;김인수
    • 한국산업융합학회 논문집
    • /
    • 제5권2호
    • /
    • pp.103-110
    • /
    • 2002
  • An artificial neural network is applied to the prediction of compressive strength, slump value of concrete. Standard mixed tables arc trained and estimated, and the results are compared with those of experiments. To consider the varieties of material properties, the standard mixed tables of two companies of Ready Mixed Concrete are used. And they are trained with the neural network. In this paper, standard back propagation network is used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of 210, $240kgf/cm^2$ and target slump value of 12, 15cm are used because the amount of production of that range arc the most at ordinary companies. In results, in the prediction of compressive strength and slump value, the predicted values are converged well to those of standard mixed tables at the target error of 0.10, 0.05, 0.001 regardless of two companies.

  • PDF

MINERAL POTENTIAL MAPPING AND VERIFICATION OF LIMESTONE DEPOSITS USING GIS AND ARTIFICIAL NEURAL NETWORK IN THE GANGREUNG AREA, KOREA

  • Oh, Hyun-Joo;Lee, Sa-Ro
    • 대한원격탐사학회:학술대회논문집
    • /
    • 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
    • /
    • pp.710-712
    • /
    • 2006
  • The aim of this study was to analyze limestone deposits potential using an artificial neural network and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential deposits in the Gangreung area, Korea. A spatial database considering deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The factors relating to 44 limestone deposits were the geological data, geochemical data and geophysical data. These factors were used with an artificial neural network to analyze mineral potential. Each factor’s weight was determined by the back-propagation training method. Training area was applied to analyze and verify the effect of training. Then the mineral deposit potential indices were calculated using the trained back-propagation weights, and potential map was constructed from GIS data. The mineral potential map was then verified by comparison with the known mineral deposit areas. The verification result gave accuracy of 87.31% for training area.

  • PDF

신경회로망을 적용한 가스터빈 엔진의 성능진단 연구 (A Study on Performance Diagnostics of a Gas Turbine Engine Using Neural Network)

  • 공창덕;고성희;기자영;강명철
    • 한국추진공학회:학술대회논문집
    • /
    • 한국추진공학회 2003년도 제21회 추계학술대회 논문집
    • /
    • pp.267-270
    • /
    • 2003
  • 본 연구에서는 신경회로망을 이용한 가스터빈 엔진의 지능형 성능 진단 컴퓨터 프로그램을 개발하였다. 최근에는 엔진 손상을 분석하는데 있어서 주요 구성품의 가스 경로를 실시간 모니터링(monitoring)하는 가스경로해석(GPA, Gas Path Analysis)방법이 사용되고 있다. 그러나 엔진손상의 형태나 정도가 다양하고 복잡하기 때문에 가스경로해석 접근법만 가지고서는 엔진의 손상상태를 모두 모니터링하기란 쉽지 않다. 따라서 이 문제를 해결하기 위해 학습과 진단을 할 수 있는 신경회로망을 적용하였다. 본 연구에서는 PT6A-62 터보프롭 엔진의 진단에 1개의 은닉층을 갖는 역전파 신경회로망(BPN, Back Propagation Neural Network)이 제안되었다.

  • PDF

칼만 필터와 뉴럴 네트워크 모델링을 이용한 연속생산공정의 통계적 공정관리 시스템 (Statistical Process Control System for Continuous Flow Processes Using the Kalman Filter and Neural Network′s Modeling)

  • 권상혁;김광섭;왕지남
    • 한국정밀공학회지
    • /
    • 제15권3호
    • /
    • pp.50-60
    • /
    • 1998
  • This paper is concerned with the design of two residual control charts for real-time monitoring of the continuous flow processes. Two different control charts are designed under the situation that observations are correlated each other. Kalman-Filter based model estimation is employed when the process model is known. A black-box approach, based on Back-Propagation Neural Network, is also applied for the design of control chart when there is no prior information of process model. Performance of the designed control charts and traditional control charts is evaluated. Average run length(ARL) is adopted as a criterion for comparison. Experimental results show that the designed control chart using the Neural Network's modeling has shorter ARL than that of the other control charts when process mean is shifted. This means that the designed control chart detects the out-of-control state of the process faster than the others. The designed control chart using the Kalman-Filter based model estimation also has better performance than traditional control chart when process is out-of-control state.

  • PDF