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Propagation Neural Network (propagation + neural_network)

Distribution by Scientific Domains
Engineering50%

Kinds of Propagation Neural Network

  • back propagation neural network
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    Selected Abstracts

    Use of neural networks for the prediction of frictional drag and transmission of axial load in horizontal wellbores

    INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 2 2003
    Tanvir Sadiq
    Abstract The use of mud motors and other tools to accomplish forward motion of the bit in extended reach and horizontal wells allows avoiding large amounts of torque caused by rotation of the whole drill string. The forward motion of the drill string, however, is resisted by excessive amount of friction. In the presence of large compressive axial loads, the drill pipe or coiled tubing tends to buckle into a helix in horizontal boreholes. This causes additional frictional drag resisting the transmission of axial load (resulting from surface slack-off force) to the bit. As the magnitude of the frictional drag increases, a buckled pipe may become ,locked-up' making it almost impossible to drill further. In case of packers, the frictional drag may inhibit the transmission of set-up load to the packer. A prior knowledge of the magnitude of frictional drag for a given axial load and radial clearance can help avoid lock-up conditions and costly failure of the tubular. In this study a neural network model, for the prediction of frictional drag and axial load transmission in horizontal wellbores, is presented. Several neural network architectures were designed and tested to obtain the most accurate prediction. After cross-validation of the Back Propagation Neural Network (BPNN) algorithm, a two-hidden layer model was chosen for simultaneous prediction of frictional drag and axial load transmission. A comparison of results obtained from BPNN and General Regression Neural Network (GRNN) algorithms is also presented. Copyright © 2002 John Wiley & Sons, Ltd. [source]

    Prediction of Hydrogen Content in Coal using Back Propagation Neural Network

    ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 1-2 2005
    L. C. Ju
    This paper introduces the concept of sample study risk in neural network (NN), and studies the prediction of hydrogen content in coal using Back Propagation Neural Networks (BP NN). Targeting the problem of training convergence quality impaired by the interfering information of some samples in BP NN, the validity of the concept of sample study in NN, and the feasibility of analyzing chemical elements in coal using NN are discussed. [source]

    Behaviour control of modern composite structures

    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 14 2004
    Alexander Tesar
    Abstract Numerical and experimental assessment of modern composite structures provided with knowledge-based joints for their behaviour control is treated in the present paper. Special connection strip, joining composite materials, is adopted. The wave approach of the back propagation neural network in micro- and macromechanical modelling is used for the numerical analysis of the problem. Some numerical and experimental results are given in order to demonstrate the efficiency of the control joint suggested. Copyright © 2004 John Wiley & Sons, Ltd. [source]