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Gotowe bibliografie tematyczne / Multivariate Response Surface

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Artykuły w czasopismach

Gotowa bibliografia na temat „Multivariate Response Surface”

Autor: Grafiati

Data publikacji: 8 września 2023

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Artykuły w czasopismach na temat "Multivariate Response Surface"

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Moslemi, Amir, and Mirmehdi Seyyed-Esfahani. "A novel robust multivariate regression approach to optimize multiple surfaces." RAIRO - Operations Research 52, no. 4-5 (2018): 1233–43. http://dx.doi.org/10.1051/ro/2018016.

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Response surface methodology involves relationships between different variables, specifically experimental inputs as controllable factors, and a response or responses by incorporating uncontrollable factors named nuisance. In order to optimize these response surfaces, we should have accurate response models. A common approach to estimate a response surface is the ordinary least squares (OLS) method. Since OLS is very sensitive to outliers, some robust approaches have been discussed in the literature. Most problems face with more than one response which are mostly correlated, that are called mu

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Bratchell, N. "Multivariate response surface modelling by principal components analysis." Journal of Chemometrics 3, no. 4 (1989): 579–88. http://dx.doi.org/10.1002/cem.1180030406.

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Moslemi, Amir, and Mirmehdi Seyyed-Esfahani. "Robust optimization of multistage process: response surface and multi-response optimization approaches." International Journal of Nonlinear Sciences and Numerical Simulation 23, no. 2 (2021): 163–75. http://dx.doi.org/10.1515/ijnsns-2017-0003.

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Abstract A multistage system refers to a system contains multiple components or stages which are necessary to finish the final product or service. To analyze these problems, the first step is model building and the other is optimization. Response surfaces are used to model multistage problem as an efficient procedure. One regular approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. OLS method is very sensitive to outliers, so some multivariate robust estimation methods have been discussed in the literature in order to estimate the respo

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Kumar, Rupak, and Meega Reji. "Response surface methodology (RSM): An overview to analyze multivariate data." Indian Journal of Microbiology Research 9, no. 4 (2023): 241–48. http://dx.doi.org/10.18231/j.ijmr.2022.042.

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In recent years, the fascinating range of Response surface methodology (RSM) applications has captured the interest of many researchers and engineers worldwide. RSM is entirely based on well-known regression principles and variance analysis principles that enable the user to improve, develop and optimize the process or product under study. An overview of the theoretical principles of RSM, the experimental strategy and its tools and components, along with the applications and pros and cons, are described in this paper. Some of the widely used experimental designs of RSM compared in terms of its

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Patel, Trina, Donatello Telesca, Saji George, and André E. Nel. "Toxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling." Annals of Applied Statistics 6, no. 4 (2012): 1707–29. http://dx.doi.org/10.1214/12-aoas563.

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Flandrois, C., C. Lahet, D. Feldmann, J. M. Gabastou, A. Gonnon, and I. Maire. "Urinary alanine aminopeptidase assay improved as result of multivariate response-surface analysis." Clinical Chemistry 34, no. 5 (1988): 954–57. http://dx.doi.org/10.1093/clinchem/34.5.954.

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Abstract Optimization of determination of alanine aminopeptidase in urine by univariate study led to a method involving pretreatment of urine with Sephadex G50. Re-examination of the optimization by multivariate study led us to recommend higher optimal concentrations: 5.8 mmol/L for the substrate and 300 mmol/L for the Tris buffer. Under these new conditions, pretreatment of urine was no longer necessary and the assay could be completely automated.

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Gatley-Montross, Caitlyn M., John A. Finlay, Nick Aldred, et al. "Multivariate analysis of attachment of biofouling organisms in response to material surface characteristics." Biointerphases 12, no. 5 (2017): 051003. http://dx.doi.org/10.1116/1.5008988.

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Li, Yingjie, Xiangliang Liu, Biaojun Zhang, Qun Zhao, Ping Ning, and Senlin Tian. "Aquatic photochemistry of sulfamethazine: multivariate effects of main water constituents and mechanisms." Environmental Science: Processes & Impacts 20, no. 3 (2018): 513–22. http://dx.doi.org/10.1039/c7em00548b.

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Ghattas, Badih, and Diane Manzon. "Machine Learning Alternatives to Response Surface Models." Mathematics 11, no. 15 (2023): 3406. http://dx.doi.org/10.3390/math11153406.

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In the Design of Experiments, we seek to relate response variables to explanatory factors. Response Surface methodology (RSM) approximates the relation between output variables and a polynomial transform of the explanatory variables using a linear model. Some researchers have tried to adjust other types of models, mainly nonlinear and nonparametric. We present a large panel of Machine Learning approaches that may be good alternatives to the classical RSM approximation. The state of the art of such approaches is given, including classification and regression trees, ensemble methods, support vec

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Murakami, Kenya, Tatsuya Iizaka, Tomoji Kanno, Tetsuro Matsui, Makoto Shimosawa, and Akihiro Takano. "Improvement of Solar Cells Efficiency by Response Surface Method based on Multivariate Analysis Models." IEEJ Transactions on Electronics, Information and Systems 131, no. 8 (2011): 1424–30. http://dx.doi.org/10.1541/ieejeiss.131.1424.

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