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Integration of functional link neural networks into a parameter estimation methodology

Authors
Le, T.-H.Tang, M.Jang, J.H.Jang, H.Shin, S.
Issue Date
10월-2021
Publisher
MDPI
Keywords
Estimation; Functional link neural network; Response surface methodology
Citation
Applied Sciences (Switzerland), v.11, no.19
Journal Title
Applied Sciences (Switzerland)
Volume
11
Number
19
URI
https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/9572
DOI
10.3390/app11199178
ISSN
2076-3417
2076-3417
Abstract
In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input?output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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