• Title/Summary/Keyword: backpropagation algorithm

Search Result 350, Processing Time 0.025 seconds

The Performance Comparison of Classifier Algorithm for Pattern Recognition of Welding Flaws (용접결함의 패턴인식을 위한 분류기 알고리즘의 성능 비교)

  • Yoon, Sung-Un;Kim, Chang-Hyun;Kim, Jae-Yeol
    • Transactions of the Korean Society of Machine Tool Engineers
    • /
    • v.15 no.3
    • /
    • pp.39-44
    • /
    • 2006
  • In this study, we nodestructive test based on ultrasonic test as inspection method and compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as pattern recognition algorithm of welding flasw. For this purpose, variables are applied the same to two algorithms. Where, feature variables are zooming flaw signals of reflected whole signals from welding flaws in time domain. Through this process, we confirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

ADALINE Controller Using Fuzzy-Backpropagation Algorithm (퍼지-역전파 알고리즘을 이용한 ADALINE 제어기)

  • 강성호;정성부;김주웅;엄기환
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2001.05a
    • /
    • pp.684-687
    • /
    • 2001
  • In this paper, we propose a ADALINE controller using fuzzy-backpropagation algorithm to adjust weight. In the proposed ADALINE controller, using fuzzy algorithm for traning neural network, controller make use of ADALINE due to simple and computing efficiency. And then it applies to servo-motor as an controlled process. And then it take a simulation for the position control, so the verify the usefulness of the proposed ADALINE controller.

  • PDF

Adaptive Blocking Artifacts Reduction Algorithm in Block Boundary Area Using Error Backpropagation Learning Algorithm (오류 역전파 학습 알고리듬을 이용한 블록경계 영역에서의 적응적 블록화 현상 제거 알고리듬)

  • 권기구;이종원;권성근;반성원;박경남;이건일
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.26 no.9B
    • /
    • pp.1292-1298
    • /
    • 2001
  • 본 논문에서는 공간 영역에서의 블록 분류 (block classification)와 순방향 신경망 필터(feedforward neural network filter)를 이용한 블록 기반 부호화에서의 적응적 블록화 현상 제거 알고리듬을 제안하였다. 제안한 방법에서는 각 블록 경계를 인접 블록간의 통계적 특성을 이용하여 평탄 영역과 에지 영역으로 분류한 후, 각 영역에 대하여 블록화 현상이 발생하였다고 분류된 클래스에 대하여 적응적인 블록간 필터링을 수행한다. 즉, 평탄 영역으로 분류된 영역 중 블록화 현상이 발생한 영역은 오류 역전파 학습 알고리듬 (error backpropagation learning algorithm)에 의하여 학습된 2계층 (2-layer) 신경망 필터를 이용하여 블록화 현상을 제거하고, 복잡한 영역으로 분류된 영역 중 블록화 현상이 발생한 영역은 에지 성분을 보존하기 위하여 선형 내삽을 이용하여 블록간 인접 화소의 밝기 값만을 조정함으로써 블록화 현상을 제거한다. 모의 실험 결과를 통하여 제안한 방법이 객관적 화질 및 주관적 화질 측면에서 기존의 방법보다 그 성능이 우수함을 확인하였다.

  • PDF

Face Detection Tracking in Sequential Images using Backpropagation (역전파 신경망을 이용한 동영상에서의 얼굴 검출 및 트래킹)

  • 지승환;김용주;김정환;박민용
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 1997.11a
    • /
    • pp.124-127
    • /
    • 1997
  • In this paper, we propose the new face detection and tracking angorithm in sequential images which have complex background. In order to apply face deteciton algorithm efficently, we convert the conventional RGB coordiantes into CIE coordonates and make the input images insensitive to luminace. And human face shapes and colors are learned using ueural network's backpropagation. For variable face size, we make mosaic size of input images vary and get the face location with various size through neural network. Besides, in sequential images, we suggest face motion tracking algorithm through image substraction processing and thresholding. At this time, for accurate face tracking, we use the face location of previous. image. Finally, we verify the real-time applicability of the proposed algorithm by the simple simulation.

  • PDF

The Development of Pattern Classification for Inner Defects in Semiconductor packages by Self-Organizing map (자기조직화 지도를 이용한 반도체 패키지 내부결함의 패턴분류 알고리즘 개발)

  • 김재열;윤성운;김훈조;김창현;송경석;양동조
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
    • /
    • 2002.10a
    • /
    • pp.80-84
    • /
    • 2002
  • In this study, researchers developed the est algorithm for artificial defects in the semic packages and performed to it by pattern recogn technology. For this purpose, this algorithm was I that researcher made software with matlab. The so consists of some procedures including ultrasonic acquistion, equalization filtering, self-organizing backpropagation neural network. self-organizing ma backpropagation neural network are belong to metho neural networks. And the pattern recognition tech has applied to classify three kinds of detective pa semiconductor packages. that is, crack, delaminat normal. According to the results, it was found estimative algorithm was provided the recognition r 75.7%( for crack) and 83.4%( for delamination) 87.2 % ( for normal).

  • PDF

A study on time-varying control of learning parameters in neural networks (신경망 학습 변수의 시변 제어에 관한 연구)

  • 박종철;원상철;최한고
    • Proceedings of the Korea Institute of Convergence Signal Processing
    • /
    • 2000.12a
    • /
    • pp.201-204
    • /
    • 2000
  • This paper describes a study on the time-varying control of parameters in learning of the neural network. Elman recurrent neural network (RNN) is used to implement the control of parameters. The parameters of learning and momentum rates In the error backpropagation algorithm ate updated at every iteration using fuzzy rules based on performance index. In addition, the gain and slope of the neuron's activation function are also considered time-varying parameters. These function parameters are updated using the gradient descent algorithm. Simulation results show that the auto-tuned learning algorithm results in faster convergence and lower system error than regular backpropagation in the system identification.

  • PDF

The Estimation of Link Travel Speed Using Hybrid Neuro-Fuzzy Networks (Hybrid Neuro-Fuzzy Network를 이용한 실시간 주행속도 추정)

  • Hwang, In-Shik;Lee, Hong-Chul
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.26 no.4
    • /
    • pp.306-314
    • /
    • 2000
  • In this paper we present a new approach to estimate link travel speed based on the hybrid neuro-fuzzy network. It combines the fuzzy ART algorithm for structure learning and the backpropagation algorithm for parameter adaptation. At first, the fuzzy ART algorithm partitions the input/output space using the training data set in order to construct initial neuro-fuzzy inference network. After the initial network topology is completed, a backpropagation learning scheme is applied to optimize parameters of fuzzy membership functions. An initial neuro-fuzzy network can be applicable to any other link where the probe car data are available. This can be realized by the network adaptation and add/modify module. In the network adaptation module, a CBR(Case-Based Reasoning) approach is used. Various experiments show that proposed methodology has better performance for estimating link travel speed comparing to the existing method.

  • PDF

Improved Error Backpropagation by Elastic Learning Rate and Online Update (가변학습율과 온라인모드를 이용한 개선된 EBP 알고리즘)

  • Lee, Tae-Seung;Park, Ho-Jin
    • Proceedings of the Korean Information Science Society Conference
    • /
    • 2004.04b
    • /
    • pp.568-570
    • /
    • 2004
  • The error-backpropagation (EBP) algerithm for training multilayer perceptrons (MLPs) is known to have good features of robustness and economical efficiency. However, the algorithm has difficulty in selecting an optimal constant learning rate and thus results in non-optimal learning speed and inflexible operation for working data. This paper Introduces an elastic learning rate that guarantees convergence of learning and its local realization by online upoate of MLP parameters Into the original EBP algorithm in order to complement the non-optimality. The results of experiments on a speaker verification system with Korean speech database are presented and discussed to demonstrate the performance improvement of the proposed method in terms of learning speed and flexibility fer working data of the original EBP algorithm.

  • PDF

Learning an Artificial Neural Network Using Dynamic Particle Swarm Optimization-Backpropagation: Empirical Evaluation and Comparison

  • Devi, Swagatika;Jagadev, Alok Kumar;Patnaik, Srikanta
    • Journal of information and communication convergence engineering
    • /
    • v.13 no.2
    • /
    • pp.123-131
    • /
    • 2015
  • Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.