Open Access
| Issue |
E3S Web Conf.
Volume 111, 2019
CLIMA 2019 Congress
|
|
|---|---|---|
| Article Number | 04054 | |
| Number of page(s) | 7 | |
| Section | High Energy Performance and Sustainable Buildings, Simulation models and predictive tools for the buildings HVAC, IEQ and energy | |
| DOI | https://doi.org/10.1051/e3sconf/201911104054 | |
| Published online | 13 August 2019 | |
- J. Merrit, J. Reiley, Preventive Conservation for Historic House Museums, p. 74, (Altamira Press, 2010) [Google Scholar]
- S. Park, Heating Ventilating and Cooling Historic Buildings, The Preservation of Historic Architecture, p. 261, (The Lyons Press, 2004) [Google Scholar]
- CEN, EN 15757:2010, Conservation of cultural property, (Beuth, 2010) [Google Scholar]
- M. Mecklenburg, Determining the Acceptable Ranges of Relative Humidity and Temperature in Museums and Galleries, p. 10, (Smithsonian Libraries, 2007) [Google Scholar]
- International Association for Science and Technology of Building Maintenance and the Preservation of Monuments (WTA), Climate and climate stability in historic buildings, Guideline 6-12, (2010) [Google Scholar]
- W. McCulloch, W. Pitts, Bulletin of Mathematical Biophysics, Vol. 5, A logical calculus of the ideas immanent in nervous activity, p. 115-133, (Kluwer, 1943) [Google Scholar]
- R. Kruse, et. Al., Computational Intelligence, p. 62, (Springer, 2015) [Google Scholar]
- P. Pardalos, H. Romeijn, Handbook of Global Optimization, p. 373, (Springer, 2002) [Google Scholar]
- F. Hofmann, R. Mikut, A. Kroll, et. Al., Computational Intelligence: State-of-the-Art Methoden und Benchmarkprobleme, (KIT Scientific Publishing 2012) [Google Scholar]
- K. Gnana Sheela, S. N. Deepa, Review on Methods to Fix Number of Hidden Neurons in Neural Networks, Mathematical Problems in Engineering, Article ID 425740, (2013) [Google Scholar]
- K. Hornik, Approximation Capabilities of Multilayer Feedforward Networks, Neural Networks, Volume 4, Issue 2, p. 251, (1991) [CrossRef] [MathSciNet] [Google Scholar]
- K. Stanley, R. Miikkulainen, Evolving Neural Networks through Augmenting Topologies, Evolutionary Computation, Volume 10 Issue 2, p. 99 – 127, (2002) [CrossRef] [PubMed] [Google Scholar]
- S. Harasty, S. Lambeck, A. Cavaterra, Model Predictive Control for Preventive Conservation using Artificial Neural Networks, CLIMA 2016 - proceedings of the 12th REHVA World Congress: volume 8, ID 450 (2016) [Google Scholar]
- M. Reick, M. J. Setzer, Untersuchung des Sorptionsverhaltens wohnraumumschließender Materialien, DFG Forschungsschwerpunkt-programm Bauphysik der Außenwände, p. 363-374, (Fraunhofer IRB Verlag, 2000) [Google Scholar]
- S. Harasty, S. Lambeck, T. Aissa, Einsatz künstlicher neuronaler Netze zur Modellierung unbekannter Störgrößen in der Raumklimatisierung, 24 CI Workshop Proceedings, p. 168, (2014) [Google Scholar]
- D. Luenberger, IEEE Transaction on Military Electronics. Vol. 8, p. 74–80 (1964) [Google Scholar]
- de.mathworks.com/help/deeplearning/index.html, (2018) [Google Scholar]
- A. Cavaterra, A. Böttcher, S. Lambeck, CLIMA 2019 proceedings of the 13th REHVA World Congress, (to be puplished) [Google Scholar]
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