S. Jakubek, N. Keuth:

"A Local Neuro-Fuzzy Network for High-Dimensional Models and Optimisation";

Engineering Applications of Artificial Intelligence,19(2006), S. 705 - 717.

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. In order to assess the quality of the obtained model the

algorithm also provides accurate model statistics. Different examples illustrate the

efficiency of the proposed algorithm.

One illustrative example shows how local models are adapted to the shape of the

target function in the presence of noise. A second example shows results obtained

with measurement databases of IC-engines.

Neural network models, local models, Engine modeling, Fuzzy modeling, Nonlinear models

http://aleph.ub.tuwien.ac.at/F?base=tuw01&func=find-c&ccl_term=AC06587347

Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.