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It will be shown that the solution guarantees the stability of closed-loop fuzzy control system and the reductions of data communications in both the sensor-to-controller and controller-to-actuator channels.
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Interval MADM methods help in reduction of data degeneration and produce more robust results.
The result is a reduction of data richness, limitations of query capability and increased systems overhead.
The main result of the use of the PDPT framework is a reduction of data transfer speed.
When traffic congestion occurs, the above methods lead to the reduction of data sensing rates in the terminal sensor nodes.
Nonlinear dimensionality reduction of data lying on multi-cluster manifolds is a crucial issue in manifold learning research.
This sequence of events violates the assumptions used in the reduction of data from common mode III tests.
However, the data points will be sparser and the number of outliers will be increased due to the reduction of data sets.
The reduction of data set is based on linear transformations and the maximisation of PCs is made in the detriment of others with less variability.
The average power reduction of data memory is 60.8 %, with a minimum of 37.8 % for Huffman decoding and a maximum of 73.2 % for IP check.
Multivariate parametric images obtained by statistical modelling of the relevant parameters allow the reduction of data and a clear definition of the defined disease target.
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