Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identification of an Open-loop Plasma Vertical Position Using Fractional
  Order Dynamic Neural Network   In order to identify complicated systems, more prominent and promising
methods are needed among which we may refer to fractional order differential
equations. The aim of this paper is to propose a fractional order nonlinear
model to predict the vertical position of a plasma column system in a Tokamak
by using real data from Damavand Tokamak. The system is identified based on a
newly introduced fractional order dynamic neural network. The proposed
fractional order dynamic neural network (FODNN) is an extension of the integer
order dynamic neural network that employs the so called fractional-order
operators. FODNN is implemented and comparison of the numerical simulation
results with experimental results shows that performance of the proposed method
by using fractional order neural network is preferred to the integer neural
network.

12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph