Artificial neural networks in structural dynamics: A new modular radial basis function approach vs. convolutional and feedforward topologies
| Autor: | Franz Bamer, Rutwik Gulakala, Marcus Stoffel, Bernd Markert |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
Artificial neural network Series (mathematics) business.industry Computer science Mechanical Engineering Computer Science::Neural and Evolutionary Computation Computational Mechanics Feed forward General Physics and Astronomy 010103 numerical & computational mathematics Modular design Network topology 01 natural sciences Convolutional neural network Computer Science Applications 010101 applied mathematics Mechanics of Materials Feedforward neural network Radial basis function Artificial intelligence 0101 mathematics business |
| Zdroj: | Computer Methods in Applied Mechanics and Engineering. 364:112989 |
| ISSN: | 0045-7825 |
| DOI: | 10.1016/j.cma.2020.112989 |
| Popis: | The aim of the present study is to develop a series of artificial neural networks (ANN) and to determine, by comparison to experiments, which type of neural network is able to predict the measured structural deformations most accurately. For this approach, three different ANNs are proposed. Firstly, the classical form of an ANN in the form of a feedforward neural network (FFNN). In the second approach a new modular radial basis function neural network (RBFNN) is proposed and the third network consists of a deep convolutional neural network (DCNN). By means of comparative calculations between neural network enhanced numerical predictions and measurements, the applicability of each type of network is studied. |
| Databáze: | OpenAIRE |
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