• Title/Summary/Keyword: back propagation algorithm

Search Result 896, Processing Time 0.027 seconds

Orientation Control of Mobile Robot Using Fuzzy-Neural Control Technique (퍼지-뉴럴 제어기법에 의한 이동형 로봇의 자세 제어)

  • 김종수
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
    • /
    • 1997.10a
    • /
    • pp.82-87
    • /
    • 1997
  • This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

  • PDF

Performance Evaluation of Chip Breaker Utilizing Neural Network (신경망기법에 의한 칩브레이커의 성능평가)

  • Kim, Hong-Gyoo;Sim, Jae-Hyung
    • Transactions of the Korean Society of Machine Tool Engineers
    • /
    • v.16 no.3
    • /
    • pp.64-74
    • /
    • 2007
  • The continuous chip in turning operation deteriorates precision of workpiece and causes a hazardous condition to operator. Thus the chip form control becomes a very important task for reliable machining process. So, grooved chip breaker is widely used to obtain reliable discontinuous chip. However, developing new cutting insert having chip breaker takes long time and needs lots of research expense due to a couple of processes such as forming, sintering, grinding and coating of product and many different evaluation tests. In this paper, performance of commercial chip breaker is evaluated with neural network which is learned with a back propagation algorithm. For the evaluation, several important elements(depth of cut, land, breadth, radius) which directly influence the chip formation were chosen among commercial chip breakers and were used as input values of neural network. With the results of these input values, the performance evaluation method was developed and applied that method to the commercial tools.

The Azimuth and Velocity Control of a Mobile Robot with Two Drive Wheels by Neural-Fuzzy Control Method (뉴럴-퍼지제어기법에 의한 두 구동휠을 갖는 이동형 로보트의 자세 및 속도 제어)

  • Cho, Y.G.;Bae, J.I.
    • Journal of Power System Engineering
    • /
    • v.2 no.3
    • /
    • pp.74-82
    • /
    • 1998
  • This paper presents a new approach to the design of speed and azimuth control of a mobile robot with two drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the neural-fuzzy network and back propagation algorithm to train the neural-fuzzy network controller in the framework of the specialized learning architecture. It is proposed to a learned controller with two neural-fuzzy networks based on an independent reasoning and a connection net with fixed weights to simplify the neural-fuzzy network. The performance of the proposed controller can be seen by the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

  • PDF

Time Series Prediction Using a Multi-layer Neural Network with Low Pass Filter Characteristics (저주파 필터 특성을 갖는 다층 구조 신경망을 이용한 시계열 데이터 예측)

  • Min-Ho Lee
    • Journal of Advanced Marine Engineering and Technology
    • /
    • v.21 no.1
    • /
    • pp.66-70
    • /
    • 1997
  • In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

  • PDF

Multi-temporal Remote-Sensing Imag e ClassificationUsing Artificial Neural Networks (인공신경망 이론을 이용한 위성영상의 카테고리분류)

  • Kang, Moon-Seong;Park, Seung-Woo;Lim, Jae-Chon
    • Proceedings of the Korean Society of Agricultural Engineers Conference
    • /
    • 2001.10a
    • /
    • pp.59-64
    • /
    • 2001
  • The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.

  • PDF

Automatic segmentation of magnetic resonance images using error back-propagation algorithm (오류 역전파 알고리즘을 이용한 자기 공명 영상 자동 세그멘테이션)

  • 최재호;조범준
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.22 no.11
    • /
    • pp.2425-2431
    • /
    • 1997
  • The increased usage of Magnetic Resonance Image (MRI) required the method for automatic segmentation of medical image that is more useful so as to diagnose the dissecitive information of a atient quickly and effectively through MR scans.The use of neural networks may give much hep to solving the complex problems concerned the matter. This paper proposes the new method for automatic segmentation of magnetic resonance (MR) images of the brain by using neural networks brained by back-propagation algorithm. The trained neural networks by the segmenting MR images of a patient produce an output that networks can segment MR images of the other patients automatically, too and show a clear image of the brain.

  • PDF

A Study for Snoring Detection Based Artificial Neural Network (신경망 기반의 코골이 검출 알고리즘 개발에 관한 연구)

  • Jang, Won-Kyu;Cho, Sung-Pil;Lee , Kyung-Joung
    • The Transactions of the Korean Institute of Electrical Engineers D
    • /
    • v.51 no.7
    • /
    • pp.327-333
    • /
    • 2002
  • In this study, we developed a snoring detection algorithm that detects snores automatically. It consists of preprocessing and snoring detection part. The preprocessing part is composed of a noise removal part using spectrum subtraction, and segmentation part, and computation part of temporal and spectral features. And the snoring detection part decides whether detected blocks are snores with BPNN(Back-Propagation Neural Network). BPNN with one hidden layer and one output layer, is trained with data of 7 subjects and tested with data of 11 subjects of total 18 subjects. The proposed algorithm showed a Sensitivity of 90.41% and a Predictive Positive Value of 84.95%.

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
    • /
    • v.32B no.6
    • /
    • pp.922-931
    • /
    • 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.

  • PDF

The Basic Design of High Speed Neural Network Filter for Application of Machine Tools Controller (공작기계 컨트롤러용 고속 신경망 필터의 기초설계)

  • 김진선;신우철;홍준희
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
    • /
    • 2003.10a
    • /
    • pp.125-130
    • /
    • 2003
  • This Paper describes a Nonlinear adoptive noise canceller using Neural Network for Machine Tools Controller System. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this Paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental results show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary Input is divided by Unit and each divided pan is processed for very short time than all the processed data are unified to whole data.

  • PDF

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
    • /
    • v.12 no.11
    • /
    • pp.118-124
    • /
    • 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

  • PDF