Emissionsauslegung des dieselmotorischen Fahrzeugantriebs mittels DoE und Simulationsrechnung

Bittermann, Andreas; Kranawetter, Erwin; Krenn, Johann; Ladein, Bernhard; Ebner, Thomas; Altenstrasser, Harald; Koegeler, Hans-Michael; Gschweitl, Kurt

doi:10.1007/bf03227193

Cited by 12 publications

(1 citation statement)

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“…In the practical application that motivated this work the amount of data available for identification is limited and the distribution of the data in their input space is sparse [5]. This imposes a limit on the network construction algorithm and it can lead to the situation that many local models are built where much fewer would be sufficient.…”

Section: Introductionmentioning

confidence: 99%

A New Training Algorithm for Neuro-Fuzzy Networks

Jakubek

Keuth²

2005

Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing

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In this paper a new iterative construction algorithm for local model networks is presented. The algorithm is focussed on building models with sparsely distributed data as they occur in engine optimization processes. The validity function of each local model is fitted to the available data using statistical criteria along with regularisation and thus allowing an arbitrary orientation and extent in the input space. Local models are consecutively placed into those regions of the input space where the model error is still large thus guaranteeing maximal improvement through each new local model. The orientation and extent of each validity function is also adapted to the available training data such that the determination of the local regression parameters is a well posed problem. The regularisation of the model can be controlled in a distinct manner using only two user-defined parameters. Examples from an industrial problems illustrate the efficiency of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%