R. Šindelář (Czech Republic)
- fuzzy modelling, fuzzy hierarchical system
-- A simple and effective method for the selection of significant inputs in nonlinear regression models is proposed. Given a set of inputoutput data and an initial superset of potential inputs, the relevant inputs are selected by checking whether after deleting a particular input, the data set is still consistent with the basic property of a function. In order to be able to handle real-valued an possibly noisy data in a sensible manner, fuzzy clustering is first applied. The obtained clusters are compared by using a similarity measure in order to find inconsistencies within the data. Several examples using simulation as well as real data are presented to demonstrate the effectiveness of the algorithm.
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