Interval polynomial regression by use of a neural network for minimum zone problems

NCUIR > Engineering College > Graduate Institute of Mechanical Engineering > journal & Dissertation > Item 987654321/26965

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/26965

Title:	Interval polynomial regression by use of a neural network for minimum zone problems
Authors:	Suen,DS;Chang,CN
Contributors:	機械工程研究所
Keywords:	ENGINEERING SURFACES;FORM TOLERANCES;STRAIGHTNESS;ERRORS;VERIFICATION;FLATNESS
Date:	1998
Issue Date:	2010-06-29 18:04:21 (UTC+8)
Publisher:	中央大學
Abstract:	Determining the parametrization of the curve is a fundamental problem in approximation and interpolation. The goal of this paper is to develop an accurate and robust algorithm for the minimum zone problems. In this paper, we use an interval bias adaptive linear neural network structure together with an appropriate cost function and the least mean squares learning algorithm to carry out the interval regression analysis. Through appropriate choice of the output function of the input neuron, the interval polynomial regression use of a neural network (IPRNN) method developed in this paper is applicable to many problems (interval algebraic polynomial approximation, evaluation of straightness, roundness and ellipticity and so on). Generally, these problems have complicated constraints and the LSQ method cannot be used.
Relation:	MEASUREMENT SCIENCE & TECHNOLOGY
Appears in Collections:	[Graduate Institute of Mechanical Engineering] journal & Dissertation

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