Korean Journal of Computational Design and Engineering (한국CDE학회논문집)

Society for Computational Design and Engineering (한국CDE학회)
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Genetic Programming Approach to Curve Fitting of Noisy Data and Its Application In Ship Design

유전적 프로그래밍을 이용한 노이지 데이터의 Curve Fitting과 선박설계에서의 적용

  • 이경호 (인하대학교 선박해양공학과) ;
  • 연윤석 (대진대학교 컴퓨터응용 설계공학과)
  • Published : 2004.09.01
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Abstract

This paper deals with smooth curve fitting of data corrupt by noise. Most research efforts have been concentrated on employing the smoothness penalty function with the estimation of its optimal parameter in order to avoid the 'overfilling and underfitting' dilemma in noisy data fitting problems. Our approach, called DBSF(Differentiation-Based Smooth Fitting), is different from the above-mentioned method. The main idea is that optimal functions approximately estimating the derivative of noisy curve data are generated first using genetic programming, and then their integral values are evaluated and used to recover the original curve form. To show the effectiveness of this approach, DBSP is demonstrated by presenting two illustrative examples and the application of estimating the principal dimensions of bulk cargo ships in the conceptual design stage.

Keywords

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