후보점과 대표점 교차검증에 의한 순차적 실험계획Candidate Points and Representative Cross-Validation Approach for Sequential Sampling
- Other Titles
- Candidate Points and Representative Cross-Validation Approach for Sequential Sampling
- Authors
- 이태희; 김승원; 정재준
- Issue Date
- Jan-2007
- Publisher
- 대한기계학회
- Keywords
- Candidate Point(후보점); Cross-Validation Approach(교차검증 실험계획); Kriging Model(크리깅모델); Representative Cross-Validation Approach(대표점 교차검증); Candidate Point(후보점); Cross-Validation Approach(교차검증 실험계획); Kriging Model(크리깅모델); Representative Cross-Validation Approach(대표점 교차검증)
- Citation
- 대한기계학회논문집 A, v.31, no.1, pp.55 - 61
- Indexed
- SCOPUS
KCI
- Journal Title
- 대한기계학회논문집 A
- Volume
- 31
- Number
- 1
- Start Page
- 55
- End Page
- 61
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180520
- DOI
- 10.3795/KSME-A.2007.31.1.055
- ISSN
- 1226-4873
- Abstract
- Recently simulation model becomes an essential tool for analysis and design of a system but it is often expensive and time consuming as it becomes complicate to achieve reliable results. Therefore, high-fidelity simulation model needs to be replaced by an approximate model, the so-called metamodel. Metamodeling techniques include 3 components of sampling, metamodel and validation. Cross-validation approach has been proposed to provide sequnatially new sample point based on cross-validation error but it is very expensive because cross-validation must be evaluated at each stage. To enhance the cross-validation of metamodel, sequential sampling method using candidate points and representative cross-validation is proposed in this paper. The candidate and representative cross-validation approach of sequential sampling is illustrated for two-dimensional domain. To verify the performance of the suggested sampling technique, we compare the accuracy of the metamodels for various mathematical functions with that obtained by conventional sequential sampling strategies such as maximin distance, mean squared error, and maximum entropy sequential samplings. Through this research we learn that the proposed approach is computationally inexpensive and provides good prediction performance.
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