• Title/Summary/Keyword: Back Propagation Training Algorithm

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Self-Relaxation for Multilayer Perceptron

  • Liou, Cheng-Yuan;Chen, Hwann-Txong
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.113-117
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    • 1998
  • We propose a way to show the inherent learning complexity for the multilayer perceptron. We display the solution space and the error surfaces on the input space of a single neuron with two inputs. The evolution of its weights will follow one of the two error surfaces. We observe that when we use the back-propagation(BP) learning algorithm (1), the wight cam not jump to the lower error surface due to the implicit continuity constraint on the changes of weight. The self-relaxation approach is to explicity find out the best combination of all neurons' two error surfaces. The time complexity of training a multilayer perceptron by self-relaxationis exponential to the number of neurons.

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Improving the Error Back-Propagation Algorithm of Multi-Layer Perceptrons with a Modified Error Function (역전파 학습의 오차함수 개선에 의한 다층퍼셉트론의 학습성능 향상)

  • 오상훈;이영직
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.32B no.6
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    • pp.922-931
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    • 1995
  • In this paper, we propose a modified error function to improve the EBP(Error Back-Propagation) algorithm of Multi-Layer Perceptrons. Using the modified error function, the output node of MLP generates a strong error signal in the case that the output node is far from the desired value, and generates a weak error signal in the opposite case. This accelerates the learning speed of EBP algorothm in the initial stage and prevents overspecialization for training patterns in the final stage. The effectiveness of our modification is verified through the simulation of handwritten digit recognition.

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Design of Initial Billet using the Artificial Neural Network for a Hot Forged Product (신경망을 이용한 열간단조품의 초기 소재 설계)

  • Kim, D.J.;Kim, B.M.;Park, J.C.
    • Journal of the Korean Society for Precision Engineering
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    • v.12 no.11
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    • pp.118-124
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    • 1995
  • In the paper, we have proposed a new technique to determine the initial billet for the forged products using a function approximation in neural network. A three-layer neural network is used and a back propagation algorithm is employed to train the network. An optimal billet which satisfied the forming limitation, minimum of incomplete filling in the die cavity, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of function approximation of the neural network. The amount of incomplete filling in the die, load and forming energy as well as effective strain are measured by the rigid-plastic finite element method. This new technique is applied to find the optimal billet size for the axisymmetric rib-web product in hot forging. This would reduce the number of finite element simulation for determining the optimal billet of forging products, further it is usefully adopted to physical modeling for the forging design

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Real-Time Control of DC Sevo Motor with Variable Load Using PID-Learning Controller (PID 학습제어기를 이용한 가변부하 직류서보전동기의 실시간 제어)

  • Kim, Sang-Hoon;Chung, In-Suk;Kang, Young-Ho;Nam, Moon-Hyon;Kim, Lark-Kyo
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.50 no.3
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    • pp.107-113
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    • 2001
  • This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

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Speech and Noise Recognition System by Neural Network (신경회로망에 의한 음성 및 잡음 인식 시스템)

  • Choi, Jae-Sung
    • The Journal of the Korea institute of electronic communication sciences
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    • v.5 no.4
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    • pp.357-362
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    • 2010
  • This paper proposes the speech and noise recognition system by using a neural network in order to detect the speech and noise sections at each frame. The proposed neural network consists of a layered neural network training by back-propagation algorithm. First, a power spectrum obtained by fast Fourier transform and linear predictive coefficients are used as the input to the neural network for each frame, then the neural network is trained using these power spectrum and linear predictive coefficients. Therefore, the proposed neural network can train using clean speech and noise. The performance of the proposed recognition system was evaluated based on the recognition rate using various speeches and white, printer, road, and car noises. In this experiment, the recognition rates were 92% or more for such speech and noise when training data and evaluation data were the different.

Prediction of Chip Forms using Neural Network and Experimental Design Method (신경회로망과 실험계획법을 이용한 칩형상 예측)

  • 한성종;최진필;이상조
    • Journal of the Korean Society for Precision Engineering
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    • v.20 no.11
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    • pp.64-70
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    • 2003
  • This paper suggests a systematic methodology to predict chip forms using the experimental design technique and the neural network. Significant factors determined with ANOVA analysis are used as input variables of the neural network back-propagation algorithm. It has been shown that cutting conditions and cutting tool shapes have distinct effects on the chip forms, so chip breaking. Cutting tools are represented using the Z-map method, which differs from existing methods using some chip breaker parameters. After training the neural network with selected input variables, chip forms are predicted and compared with original chip forms obtained from experiments under same input conditions, showing that chip forms are same at all conditions. To verify the suggested model, one tool not used in training the model is chosen and input to the model. Under various cutting conditions, predicted chip forms agree well with those obtained from cutting experiments. The suggested method could reduce the cost and time significantly in designing cutting tools as well as replacing the“trial-and-error”design method.

An application of neural network to autopilot design (신경회로망을 이용한 자동조종장치 설계)

  • 유재종;송찬호
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10a
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    • pp.619-623
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    • 1993
  • In this paper, a neural network is appled to design a lateral autopilot for airplanes. Linearized lateral dynamics is used in training the neural network controller and verifying the performance as well. To train the neural network, back propagation algorithm is used. In this training, no information about the dynamics to be controlled except sign and rough magnitude of control derivatives is needed. It is shown by computer simulations that the performance and stability margin are satisfactory.

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A Robust Propagation Algorithm for Function Approximation (함수근사를 위한 로버스트 역전파 알고리즘)

  • Kim, Sang-Min;Hwang, Chang-Ha
    • The Transactions of the Korea Information Processing Society
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    • v.4 no.3
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    • pp.747-753
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    • 1997
  • Function approximation from a set of input-output parirs has numerous applications in scientiffc and engineer-ing areas.Multiayer feedforward neural networks have been proposed as a good approximator of noninear function.The back propagation (BP) algorithm allows muktiayer feedforward neural networks oro learn input-output mappongs from training samples.However, the mapping acquired through the BP algorithm nay be cor-rupt when errorneous trauning data are employed.In this paper we propose a robust BP learning algorithm that is resistant to the errormeous data and is capable of rejecting gross errors during the approximation process.

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

  • 이준재;황석윤;김효성;이재욱;서용수
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.3
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    • pp.150-158
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    • 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.

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Experimental Studies of Neural Network Control Technique for Nonlinear Systern (신경회로망을 이용한 비선형 시스팀 제어의 실험적 연구)

  • Im, Sun-Bin;Jung, Seul
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.195-195
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    • 2000
  • In this paper, intelligent control method using neural network as a nonlinear controller is presented, Neural network controller is implemented on DSP board in PC to make real time computing possible, On-line training algorithm for neural network control is proposed, As a test-bed, a large a-x table was build and interface with PC has been implemented, Experimental results under different PD controller gains show excellent position tracking for circular trajectory compared with those for PD controller only.

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