선택한 단어 수는 입니다.
최소 단어 이상 선택하여야 합니다.
최소 단어 이상 선택하여야 합니다.
선택한 단어 수는 30입니다.
최대 10 단어까지만 선택 가능합니다.
최대 10 단어까지만 선택 가능합니다.
제안 방법
- In this research, moving least squares method (MLSM) (2, 3), a local approximation method, is adopted to reduce the approximation errors, and sensitivity information is included for the purpose of high efficiency and accuracy. This paper mainly discusses how to construct RS models efficiently and accurately using the moving least squares method (MLSM) and sensitivity.
- During the approximation using the moving least squares method (MLSM) with sensitivity information, several parameters should be determined carefully. Parametric study and optimization for these parameters, a weighting factor for gradient and size of local approximation region, were performed. Correlation coefficient and reciprocal condition number were adopted for better accuracy criteria, and a genetic algorithm was used for the parametric optimization.
- This paper mainly discussed how to construct RSM efficiently and accurately using sensitivity when the exact sensitivities were available. During the approximation using the moving least squares method (MLSM) with sensitivity information, several parameters should be determined carefully.
- accuracy. This paper mainly discusses how to construct RS models efficiently and accurately using the moving least squares method (MLSM) and sensitivity.
데이터처리
- If it is 1, X1 and X2 are perfectly correlated. In this research, a measure of the accuracy is the correlation coefficients of sampled data and estimated data from RSM.
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