Abstract
For tuning fuzzy controllers, several parameter identification techniques are available, ranging from more robust descent methods to sophisticated optimisation. However, from an application point of view, it is not always clear that numerical sophistication wins over more pragmatic approaches to tuning. Obviously, the data sets play crucial roles in efforts to reach successful tuning. Especially data sets generated from real processes often contain not only noisy data and conflicting subsets, but also the connected problem of non-covering input spaces. In this paper we will compare several parameter identification techniques w.r.t. different data sets. We focus on selections of learning rates and on defining training sequences related to subclasses of parameters.
Published Version
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