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

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题名:	Interval polynomial regression by use of a neural network for minimum zone problems
作者:	Suen,DS;Chang,CN
贡献者:	機械工程研究所
关键词:	ENGINEERING SURFACES;FORM TOLERANCES;STRAIGHTNESS;ERRORS;VERIFICATION;FLATNESS
日期:	1998
上传时间:	2010-06-29 18:04:21 (UTC+8)
出版者:	中央大學
摘要:	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.
關聯:	MEASUREMENT SCIENCE & TECHNOLOGY
显示于类别:	[機械工程研究所] 期刊論文

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