Gaussian Process Model for Control of an Existing Building

Ahn, Ki Uhn; Kim, Deuk-Woo; Kim, Young Jin; Park, Cheol Soo; Kim, In Han

doi:10.1016/j.egypro.2015.11.295

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…In Su et al (2017), GP model has been coupled with Monte Carlo simulations to perform a reliability analysis of complex engineering structures. An application of GP to control an existing building can be found in Ahn et al (2015). In Wan et al (2014), a surrogate model based on GP has been established to deal with uncertainty quantification for modal frequencies.…”

Section: Introductionmentioning

confidence: 99%

Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and Sequential Monte Carlo

Abdessalem

Dervilis

Wagg

et al. 2017

Front. Built Environ.

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The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter estimation for machine learning applications. The combined methodology that this research article proposes and investigates offers the possibility to use different metrics and summary statistics of the kernels used for Bayesian regression. The presented work moves a step toward online, robust, consistent, and automated mechanism to formulate optimal kernels (or even mean functions) and their hyperparameters simultaneously offering confidence evaluation when these tools are used for mathematical or engineering problems such as structural health monitoring (SHM) and system identification (SI).

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Section: Introductionmentioning

confidence: 99%