e knowledge on strength properties of porous metals in compression is essential in tailored application design, as well as in elaboration of general material models. In this article, the authors propose specification details of the ANN architecture for adequate modelling of the phenomenon of compressive behaviour of open-cell aluminum. In the presented research, an algorithm was used to build different structures of artificial neural networks (ANNs), which approximated stress-strain relations of an aluminum sponge subjected to compression. Next, the quality of the built approximations was appraised. e mean absolute relative error (MARE), coefficient of determination between outputs and targets (R 2 ), root mean square error (RMSE), and mean square error (MSE) were assumed as criterial measures for the assessment of the fitting quality. e studied neural networks (NNs) were two-layer feedforward networks with different numbers of neurons in the hidden layer. A set of experimental stress-strain data from quasistatic uniaxial compression tests of open-cell aluminum of various apparent densities was used as data for training of neural networks. Analysis was performed in two modes: in the first one, all samples were taken for training, and in the second case, one sample was left out during training in order to play the role of external data for testing the trained network later. e taken out samples were maximum and minimum density samples (for extrapolation) and one random from within the density interval. e results showed that good approximation on the engineering level (MARE < 5%) was reached for teaching networks with ≥7 neurons in the hidden layer for the first studied case and with ≥8 neurons for the second. Calculations on external data proved that 8 neurons are enough to actually obtain MARE < 10%. Moreover, it was shown that the quality of approximation can be significantly improved to MARE ≈ 7% (tested on external data) if the initial region of the stress-strain relation is modelled by an additional network.
In this article, we present a proposition of a model of the compressive behaviour of open-cell aluminium with relation to the material apparent density. The research was based on experimental data from uniaxial compression tests conducted for two sample lots. These results were analysed with the use of neural networks in a specially designed algorithm. The main criterion for choosing a satisfactory approximation was mean absolute relative error MARE < 5%. As a result of the analysis, the sought relation was extracted and is presented as a proposition of a new ANN model of the compressive stress-strain relationship for aluminium sponge.
Modelling of comfort with the use of neural networks in modern times has become extremely popular. In recent years, scientists have been using these methods because of their satisfactory accuracy. The article proposes a method of modelling feedforward neural networks, thanks to which it is possible to obtain the most efficient network with one hidden layer in terms of a given quality criterion. The article also presents the methodology for modelling a PMV index, on the basis of which it can be demonstrated whether the network will work properly not only on paper but in reality as well. The objective of this work is to develop a performance model allowing the effective improvement of all electrical and mechanical devices affecting the energy efficiency and indoor environment in smart buildings. To achieve this, several attributes of indoor environment are included, namely: air leakage as a connection to the outdoor environment, but also as uncontrolled component of energy, ventilation as delivery and distribution of fresh air in the building space, individual ventilation on demand indoor air quality (IAQ) in the dwelling or as a personal IAQ control, source control of pollutants in the building, thermal comfort, temperature, air movement and humidity control (humidity modifiers, i.e., buffers different from the air conditioning radiation from cold and hot surfaces bringing forward a question about the strategy of the process control. One may either develop a series of control models to be synthesized later or one can use one over-arching characteristic and use its components for operating the control system. The paper addresses the second strategy and uses the concept of PMV for a criterion of broadly defined thermal comfort (including ventilation and air quality).
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