Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of Artificial Neural Networks in Predicting Abrasion
  Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites   Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR)
based composites is a typical and crucial property in practical applications.
Previous studies show that the abrasion resistance can be calculated by the
multiple linear regression model. In our study, considering this relationship
can also be described into the non-linear conditions, a Multilayer Feed-forward
Neural Networks model with 3 nodes (MLFN-3) was successfully established to
describe the relationship between the abrasion resistance and other properties,
using 23 groups of data, with the RMS error 0.07. Our studies have proved that
Artificial Neural Networks (ANN) model can be used to predict the SSBR-based
composites, which is an accurate and robust process.

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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: stat