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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/26988


    Title: Application of neural network interval regression method for minimum zone straightness and flatness
    Authors: Suen,DS;Chang,CN
    Contributors: 機械工程研究所
    Keywords: ENGINEERING SURFACES;ERRORS
    Date: 1997
    Issue Date: 2010-06-29 18:04:56 (UTC+8)
    Publisher: 中央大學
    Abstract: The goal of this paper is to develop an accurate, efficient, and robust algorithm for the minimum zone (MZ) straightness and flatness. In this paper, we use an interval bias adaptive linear neural network (NN) structure together with least mean squares (LMS) learning algorithm, and an appropriate cost function to carry out the interval regression analysis. From the results, we can see that both the straightness and flatness results from the interval regression method by NN can converge closer to the definition of the MZ straightness and flatness, respectively, than that of the least-squares (LSQ) method. The interval regression method by NN developed in this paper is applicable in the linear regression analysis that has a complicated constraint, and where the LSQ method cannot be used. (C) Elsevier Science Inc., 1997.
    Relation: PRECISION ENGINEERING-JOURNAL OF THE AMERICAN SOCIETY FOR PRECISION ENGINEERING
    Appears in Collections:[機械工程研究所] 期刊論文

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