Effiziente Motorapplikation mit lokal linearen neuronalen Netzen

Martini, Eike; Voß, Hartmut; Töpfer, Susanne; Isermann, Rolf

doi:10.1007/bf03226705

Cited by 7 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For further optimization, potentials were identified. For example, the quality of the training data can be optimized by further optimization of the experimental design, in particular by an intelligent distribution of the measurement point density analogous to Martini et al (2003).…”

Section: Discussionmentioning

confidence: 99%

Black Box Efficiency Modelling of an Electric Drive Unit Utilizing Methods of Machine Learning

BAUER

STÜTZ²,

KLEY³

2021

acs

View full text Add to dashboard Cite

The increasing electrification of powertrains leads to increased demands for the test technology to ensure the required functions. For conventional test rigs in particular, it is necessary to have knowledge of the test technology's capabilities that can be applied in practical testing. Modelling enables early knowledge of the test rigs dynamic capabilities and the feasibility of planned testing scenarios. This paper describes the modelling of complex subsystems by experimental modelling with artificial neural networks taking transmission efficiency as an example. For data generation, the experimental design and execution is described. The generated data is pre-processed with suitable methods and optimized for the neural networks. Modelling is executed with different variants of the inputs as well as different algorithms. The variants compare and compete with each other. The most suitable variant is validated using statistical methods and other adequate techniques. The result represents reality well and enables the performance investigation of the test systems in a realistic manner.

show abstract

Section: Discussionmentioning

confidence: 99%

Black Box Efficiency Modelling of an Electric Drive Unit Utilizing Methods of Machine Learning

BAUER

STÜTZ²,

KLEY³

2021

acs

View full text Add to dashboard Cite

show abstract

“…If one knows the degree of nonlinearity from an input of a similar engine, then he easily can incorporate this knowledge in the measurmentdesign of the interested engine. A similar incorporation of prior knowledge is also possible with LOLIMOT and HHT, see Martini et al (2003). Moreover, human interpretability and incorporation of prior knowledge is more easy using tree-based models than other algorithms from machine learning, see Nelles (2001).…”

Section: Gaussian Processes Compared To Tree-based Regressionmentioning

confidence: 96%