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In this paper is analysed the problem, using soft models, of soft dependence of parameters in design systems.
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In 2011, Tsugawa et al[168] used Soft Independent Modeling of Class Analogy (SIMCA) to build multiple class models.
Using soft product operations of interval-valued fuzzy soft sets, we first investigate some basic properties of soft J-inclusions and soft L-inclusions.
This provided a wide range of conditions to study the model predictions, using soft substrates.
After a rigorous peer-review process, the 13th SOCO 2018 International Program Committee selected 41 papers, with a special emphasis on optimization, modeling and control using soft computing techniques and soft computing applications in the field of industrial and environmental enterprises.
Using soft sets as the granulation structures, Feng et al. [8] defined soft approximation spaces, soft rough approximations and soft rough sets, which are generalizations of Pawlak's rough set model based on soft sets.
(Try using Soft Scrub).
Try using soft scents.
In part we compensated for this shortcoming by using the soft modelling statistical package, NEUSREL.
Artificial neural networks are the most widely used soft computing technique for the development of these prediction models.
Only use soft cloth.
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