• 제목/요약/키워드: Backpropagation Algorithm

검색결과 350건 처리시간 0.025초

CDMA System에서 협대역 간섭제거 적응 상관기에 관한 연구 (A Study On Adaptive Correlator Receiver with Narrow-band Interferance in CDMA System)

  • 정찬주;양화섭;김용식;오승재;김재갑
    • 경영과정보연구
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    • 제3권
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    • pp.201-214
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    • 1999
  • Adaptive correlator receiver with neural network based on complex multilayer perceptron is persented for suppressing interference of narrow-band of direct spread spectrum communication systems. Recursive least square algorithm with backpropagation error is used for fast convergence and better performance in adaptive correlator scheme. According to signal noise and transmission power, computer simulation results show that bit error ratio of adaptive correlator using neural network improved that of adative transversal filter of direct sequence spread spectrum considering of jamming and narrow-band interference. Bit error ratio of adaptive correlator with neural network is reduced about 10-1 than that of adaptive transversal filter where interference versus signal ratio is 5dB.

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Airline In-flight Meal Demand Forecasting with Neural Networks and Time Series Models

  • Lee, Young-Chan
    • 한국정보시스템학회:학술대회논문집
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    • 한국정보시스템학회 2000년도 추계학술대회
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    • pp.36-44
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    • 2000
  • The purpose of this study is to introduce a more efficient forecasting technique, which could help result the reduction of cost in removing the waste of airline in-flight meals. We will use a neural network approach known to many researchers as the “Outstanding Forecasting Technique”. We employed a multi-layer perceptron neural network using a backpropagation algorithm. We also suggested using other related information to improve the forecasting performances of neural networks. We divided the data into three sets, which are training data set, cross validation data set, and test data set. Time lag variables are still employed in our model according to the general view of time series forecasting. We measured the accuracy of our model by “Mean Square Error”(MSE). The suggested model proved most excellent in serving economy class in-flight meals. Forecasting the exact amount of meals needed for each airline could reduce the waste of meals and therefore, lead to the reduction of cost. Better yet, it could enhance the cost competition of each airline, keep the schedules on time, and lead to better service.

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신경회로망 콘볼루션 복호기의 최적 성능에 대한 확률적 근사화 (Stochastic approximation to an optimal performance o fthe neural convolutional decoders)

  • 유철우;강창언;홍대식
    • 전자공학회논문지A
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    • 제33A권4호
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    • pp.27-36
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    • 1996
  • It is well known that the viterbi algorithm proposed as a mthod of decoding convolutional codes is in fact maximum likelihood (ML) and therefore optimal. But, because hardware complexity grows exponentially with the constraint length, there will be severe constraints on the implementation of the viterbi decoders. In this paper, the three-layered backpropagation neural networks are proposed as an alternative in order to get sufficiently useful performance and deal successfully with the problems of the viterbi decoder. This paper shows that the neural convolutional decoder (NCD) can make a decision in the point of ML in decoding and describes simulation results. The cause of the difference between stochastic results and simulation results is discussed, and then thefuture prospect of the NCD is described on the basis of the characteristic of the transfer function.

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FNN에 기초한 Fuzzy Self-organizing Neural Network(FSONN)의 구조와 알고리즘의 구현 (The Implementation of the structure and algorithm of Fuzzy Self-organizing Neural Networks(FSONN) based on FNN)

  • 김동원;박병준;오성권
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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    • pp.114-117
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    • 2000
  • In this paper, Fuzzy Self-organizing Neural Networks(FSONN) based on Fuzzy Neural Networks(FNN) is proposed to overcome some problems, such as the conflict between ovefitting and good generation, and low reliability. The proposed FSONN consists of FNN and SONN. Here, FNN is used as the premise part of FSONN and SONN is the consequnt part of FSONN. The FUN plays the preceding role of FSONN. For the fuzzy reasoning and learning method in FNN, Simplified fuzzy reasoning and backpropagation learning rule are utilized. The number of layers and the number of nodes in each layers of SONN that is based on the GMDH method are not predetermined, unlike in the case of the popular multi layer perceptron structure and can be generated. Also the partial descriptions of nodes can use various forms such as linear, modified quadratic, cubic, high-order polynomial and so on. In this paper, the optimal design procedure of the proposed FSONN is shown in each step and performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

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특징정보 분석을 통한 실시간 얼굴인식 (Realtime Face Recognition by Analysis of Feature Information)

  • 정재모;배현;김성신
    • 한국지능시스템학회논문지
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    • 제11권9호
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    • pp.822-826
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    • 2001
  • The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of 10 persons show that the proposed method yields high recognition rates.

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7자유도 센서차량모델 제어를 위한 비선형신경망 (Nonlinear Neural Networks for Vehicle Modeling Control Algorithm based on 7-Depth Sensor Measurements)

  • 김종만;김원섭;신동용
    • 한국전기전자재료학회:학술대회논문집
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    • 한국전기전자재료학회 2008년도 하계학술대회 논문집 Vol.9
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    • pp.525-526
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    • 2008
  • For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

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퍼지제어 시스템을 위한 인공신경망 설계 (Design of Artificial Neural Networks for Fuzzy Control System)

  • 장문석;장덕철
    • 한국정보처리학회논문지
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    • 제2권5호
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    • pp.626-633
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    • 1995
  • 퍼지 시스템 모델링에 있어서, 퍼지 규칙을 인식하고 퍼지 추론의 소속함수를 조 정하기란 매우 어렵다. 본 논문에서는 인공신경망을 이용함으로써, 자동으로 퍼지 규 칙을 인식하고 동시에 퍼지 추론의 소속함수를 조정할 수 있는 퍼지신경망 모델을 제 시하고, 인공신경망의 수렴도를 향상시키기 위해 개선된 역전파 알고리즘을 사용하여 학습에 사용하였다. 이 방법의 타당성을 로보트 매니풀레이터를 통해 검증 한다.

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역전파 알고리즘을 이용한 도립 진자 제어 (The Control of A Inverted Pendulum Using Backpropagation)

  • 최용길;홍대승;임화영
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2000년도 하계학술대회 논문집 D
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    • pp.2380-2382
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    • 2000
  • Fuzzy system which are based on membership functions and rules, can control nonlinear, uncertian, complex system well. However, Fuzzy controller has problems: It is difficult to design a stable for amateur. To update the then-part membership functions of the fuzzy controller can be designed using the error back-propagation algorithm to be minimized error. Then we could be optimized the system choosing a good performance index. The proposed fuzzy controller based on neural network is applied to control an inverted pendulum for demonstration of the robustness of proposed methodology.

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Application of artificial neural networks to the response prediction of geometrically nonlinear truss structures

  • Cheng, Jin;Cai, C.S.;Xiao, Ru-Cheng
    • Structural Engineering and Mechanics
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    • 제26권3호
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    • pp.251-262
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    • 2007
  • This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

가공시스템에서 신경회로망을 이용한 품질의 성능 개선에 관한 설계 및 구현 (Design and Implementation of the Quality Performance Improvement for Process System Using Neural Network)

  • 문희근;김영탁;김수정;김관형;탁한호;이상배
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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    • pp.179-182
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    • 2002
  • In this paper, this system makes use of the analog sensor and converts the feature of fish analog signal 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 backpropagation is used as a learning algorithm. And the neural network is learned in off-line process. Because an adaptation period of neural network is too long time when random initial weights are used, off-line learning Is induced to decrease the Progress time We confirmed this method has better performance than somewhat outdated machines.