Efficient Response Surface Modeling by Using Moving Least-Squares Method and Sensitivity

AIAA Journal ◽  
2005 ◽  
Vol 43 (11) ◽  
pp. 2404-2411 ◽  
Author(s):  
Chwail Kim ◽  
Semyung Wang ◽  
Kyung K. Choi
Keyword(s):  
Least Squares ◽  
Response Surface ◽  
Least Squares Method ◽  
Surface Modeling ◽  
Moving Least Squares ◽  
Response Surface Modeling ◽  
Moving Least Squares Method

RESPONSE SURFACE MODEL USING MOVING LEAST SQUARES METHOD

2000 ◽  
Vol 2000.4 (0) ◽  
pp. 181-186
Author(s):  
Akihiro KAMINAGA ◽  
Katsuyuki SUZUKI ◽  
Daiji FUJII ◽  
Hideomi OHTSUBO
Keyword(s):  
Least Squares ◽  
Response Surface ◽  
Least Squares Method ◽  
Response Surface Model ◽  
Moving Least Squares ◽  
Surface Model ◽  
Moving Least Squares Method

Strength design of a knuckle component using moving least-squares response-surface-based approximate optimization methods

2009 ◽  
Vol 223 (8) ◽  
pp. 1019-1032 ◽  
Author(s):  
C Y Song ◽  
J-S Lee
Keyword(s):  
Least Squares ◽  
Response Surface ◽  
Least Squares Method ◽  
Optimization Methods ◽  
Moving Least Squares ◽  
Element Analysis ◽  
Cross Sectional ◽  
Approximate Optimization ◽  
Strength Design ◽  
Moving Least Squares Method

The paper deals with the strength design of an automotive knuckle component under bump and brake loading conditions. The design problem is formulated such that cross-sectional sizing variables are determined by minimizing the weight of a knuckle component subject to stresses, deformations, and frequency constraints. The initial design model is generated on the basis of an actual vehicle specification. The finite element analysis is conducted using ABAQUS, and optimal solutions are obtained via the moving least-squares method (MLSM) in the context of response-surface-based approximate optimization. For the meta-modelling of inequality constraint functions such as stresses, deformations, and frequency, a constraint-feasible moving least-squares method (CF-MLSM) is suggested in the present study. The method of CF-MLSM, compared with a conventional MLSM, has been shown to ensure the constraint feasibility in a case where the approximate optimization process is employed. The solution results from proposed optimization methods present improved design performances under both bump and brake conditions.

scholarly journals Meshless membrane model based on the moving least-squares method

2006 ◽  
Vol 73 (2) ◽  
Author(s):  
Hiroshi Noguchi ◽  
Gerhard Gompper
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Membrane Model ◽  
Moving Least Squares ◽  
Model Based ◽  
Moving Least Squares Method

Seismic Reliability Analysis in the Framework of Metamodelling Based Monte Carlo Simulation

2017 ◽  
pp. 192-208 ◽  
Author(s):  
Shyamal Ghosh ◽  
Soham Mitra ◽  
Swarup Ghosh ◽  
Subrata Chakraborty
Keyword(s):  
Least Squares ◽  
Reliability Analysis ◽  
Response Surface ◽  
Seismic Response ◽  
Least Squares Method ◽  
Response Prediction ◽  
Seismic Intensity ◽  
Seismic Reliability ◽  
Moving Least Squares Method ◽  
Artificial Neural Network Ann

A comparative study of various metamodelling approaches namely the least squares method (LSM), moving least squares method (MLSM) and artificial neural network (ANN) based response surface method (RSM) are presented to demonstrate the effectiveness to approximate the nonlinear dynamic response of structure required for efficient seismic reliability analysis (SRA) of structures. The seismic response approximation by the LSM, MLSM and ANN based RSMs are explained with a brief note on the important issue of ground motion bin generation. The procedure adopted herein for SRA is based on the dual response surface approach. In doing so, the repetition of seismic intensity for SRA at different intensity levels is avoided by including this as one of the predictors in the seismic response prediction model. A nonlinear SDOF system has been taken up to elucidate the effectiveness of various metamodels in SRA.

scholarly journals Multi-Dimensional Moving Least Squares Method for Elasticity Problems

2012 ◽  
Vol 78 (786) ◽  
pp. 142-151
Author(s):  
Kohei SAKIHARA ◽  
Hitoshi MATSUBARA ◽  
Takaaki EDO ◽  
Hisao HARA ◽  
Genki YAGAWA
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Least Squares ◽  
Moving Least Squares Method ◽  
Elasticity Problems

A Hybrid Method Using Response Surface and Pattern Search for Design Optimization

2005 ◽  
Author(s):  
T. Zhang ◽  
K. K. Choi ◽  
S. Rahman
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Response Surface ◽  
Basis Function ◽  
Function Approximation ◽  
Least Squares Method ◽  
Order Approximation ◽  
Pattern Search ◽  
Moving Least Squares ◽  
Radial Basis

This paper presents a new method to construct response surface function and a new hybrid optimization method. For the response surface function, the radial basis function is used for a zeroth-order approximation, while new bases is proposed for the moving least squares method for a first-order approximation. For the new hybrid optimization method, the gradient-based algorithm and pattern search algorithm are integrated for robust and efficient optimization process. These methods are based on: (1) multi-point approximations of the objective and constraint functions; (2) a multi-quadric radial basis function for the zeroth-order function representation or radial basis function plus polynomial based moving least squares approximation for the first-order function approximation; and (3) a pattern search algorithm to impose a descent condition. Several numerical examples are presented to illustrate the accuracy and computational efficiency of the proposed method for both function approximation and design optimization. The examples for function approximation indicate that the multi-quadric radial basis function and the proposed radial basis function plus polynomial based moving least squares method can yield accurate estimates of arbitrary multivariate functions. Results also show that the hybrid method developed provides efficient and convergent solutions to both mathematical and structural optimization problems.

scholarly journals An Improved Moving Least Squares Method for Curve and Surface Fitting

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Lei Zhang ◽  
Tianqi Gu ◽  
Ji Zhao ◽  
Shijun Ji ◽  
Ming Hu ◽  
...  
Keyword(s):  
Least Squares ◽  
Random Error ◽  
Least Squares Method ◽  
Surface Fitting ◽  
Measured Data ◽  
Moving Least Squares ◽  
Generate Curve ◽  
Moving Least Squares Method ◽  
Curve And Surface Fitting ◽  
Mls Method

The moving least squares (MLS) method has been developed for the fitting of measured data contaminated with random error. The local approximants of MLS method only take the error of dependent variable into account, whereas the independent variable of measured data always contains random error. Considering the errors of all variables, this paper presents an improved moving least squares (IMLS) method to generate curve and surface for the measured data. In IMLS method, total least squares (TLS) with a parameterλbased on singular value decomposition is introduced to the local approximants. A procedure is developed to determine the parameterλ. Numerical examples for curve and surface fitting are given to prove the performance of IMLS method.

The complex variable interpolating moving least-squares method

2012 ◽  
Vol 219 (4) ◽  
pp. 1724-1736 ◽  
Author(s):  
Hongping Ren ◽  
Jing Cheng ◽  
Aixiang Huang
Keyword(s):  
Least Squares ◽  
Complex Variable ◽  
Least Squares Method ◽  
Moving Least Squares ◽  
Moving Least Squares Method

Integration of Moving Least Squares Method and Multilevel B-Spline Method for Scattered Data Approximation

2011 ◽  
Vol 291-294 ◽  
pp. 2245-2249
Author(s):  
Shi Ju Yan ◽  
Bin Ge
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Scattered Data ◽  
Moving Least Squares ◽  
Scattered Data Approximation ◽  
Approximation Accuracy ◽  
Data Approximation ◽  
B Spline ◽  
Moving Least Squares Method ◽  
Method Accuracy

For scattered data approximation with multilevel B-spline(MBS) method, accuracy could be enhanced by densifying control lattice. Nevertheless, when control lattice density reaches to some extent, approximation accuracy could not be enhanced further. A strategy based on integration of moving least squares(MLS) and multilevel B-spline(MBS) is presented. Experimental results demonstrate that the presented strategy has higher approximation accuracy.

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