S. Jakubek, N. Keuth:
"A Local Neuro-Fuzzy Network for High-Dimensional Models and Optimisation";
Engineering Applications of Artificial Intelligence,
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
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