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As a result we obtain a fuzzy position region where each point in the region has a degree of certainty of being the actual position of the robot.
This paper proposed a geometric synthesis approach, which is composed of fuzzy position construction, function module selection, fuzzy pole determination and quasi-similarity transformation.
The position controller consists of a feed-forward controller and a fuzzy position tracking controller (FPTC) and acts as a decoupled controller, improving position tracking performance with the help of the feed-forward controller and the FPTC.
In this paper, the design and optimization process of fuzzy position controller is supported by learning techniques derived from neural network where a radial basis function (RBF) neural network is implemented to learn fuzzy rules and membership functions with predictor of recurrent neural network (RNN) model.
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But it has little influence on solution mechanism features and not suitable for the rigid body guidance mechanisms without "center- and circle-point" structure or planar motion generation with partly fuzzy positions.
Fuzzy positioning is not due to artefacts.
The latter are more reproducible (7.5 bp) but nucleosomes in vitro reconstitute into more fuzzy positions.
In vivo, fuzzy positioning is apparent in the gene for ADE12, adenylosuccinate synthase, particularly at the middle of the coding sequence (Figure 5).
The coding region is packaged by an array of nucleosomes with increasingly fuzzy positioning towards the 3′ end.
Indeed, in the case of fuzzy positioning the maximum for IPDFT is somewhat shifted to the left, but the distribution is very flat in this region.
A previous H4S47C-anchored cleavage mapping study found fuzzy positions of nucleosomes in the central domain, but with very low cleavage density, leaving the question of nucleosome positioning unresolved (Moyle-Heyrman et al. 2013).
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