Chapter 5 - Soft computing and statistical tools for developing analytical models
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- @InCollection{SAYYAADI:2021:MAOES,
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author = "Hoseyn Sayyaadi",
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title = "Chapter 5 - Soft computing and statistical tools for
developing analytical models",
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editor = "Hoseyn Sayyaadi",
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booktitle = "Modeling, Assessment, and Optimization of Energy
Systems",
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publisher = "Academic Press",
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pages = "247--325",
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year = "2021",
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isbn13 = "978-0-12-816656-7",
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DOI = "doi:10.1016/B978-0-12-816656-7.00005-1",
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URL = "https://www.sciencedirect.com/science/article/pii/B9780128166567000051",
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keywords = "genetic algorithms, genetic programming, Soft
computing and statistical tool (SCST), Artificial
neural network (ANN), Group method of data handling
(GMDH), Genetic programming (GP), Stepwise regression
method (SRM), Multiple linear regression (MLR)",
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abstract = "Analytical modeling provides an accurate and
straightforward way of modeling. If, in a case,
governing equations can be written and solved quickly,
the analytical modeling is done by that. However, in a
lot of real cases, it is not possible to write or solve
governing equations. In such cases, as an alternative
to the numerical models with the mentioned drawbacks,
soft computing methods and statistical tools can be
employed. They not only make the calculations both fast
and straightforward but also do not need running the
development algorithm every time they are employed. In
contrast to the numerical models, they are developed
once, and then, the provided equation(s) or network can
be used for further calculations without requiring
passing the development process again. Having the
mentioned advantages, soft computing and statistical
tools are becoming popular more and more among the
researchers for modeling energy systems. By using the
developed statistical methods, further analyses such as
optimization or parametric study can be done much more
easily compared to the other models, especially
numerical models. Artificial neural networks (ANNs),
group method of data handling (GMDH), genetic
programming (GP), response surface methodology (RSM),
multiple linear regression (MLR), and stepwise
regression method (SRM) are the most common statistical
methods for modeling of energy systems. This chapter
provides information about them",
- }
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Hoseyn Sayyaadi
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