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Stefan Jakubek, Nikolaus Keuth

Improved Neuro-Fuzzy Models for Design Processes and Simulation in Automotive Applications

In this paper a new iterative partitioning 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. 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 by the user in a distinct manner by one parameter. In order to assess the quality of the obtained model the algorithm also provides model statistics. Different examples from practical applications illustrate the efficiency of the proposed algorithm.

at – Automatisierungstechnik, Oldenbourg Wissenschaftsverlag

Print ISSN: 0178-2312
Volume: 53, 09/2005
Pages: 425 - 433

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