Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Data-Driven Modeling Framework of Time-Dependent Switched Dynamical
  Systems via Extreme Learning Machine   In this work, a data-driven modeling framework of switched dynamical systems
under time-dependent switching is proposed. The learning technique utilized to
model system dynamics is Extreme Learning Machine (ELM). First, a method is
developed for the detection of the switching occurrence events in the training
data extracted from system traces. The training data thus can be segmented by
the detected switching instants. Then, ELM is used to learn the system dynamics
of subsystems. The learning process includes segmented trace data merging and
subsystem dynamics modeling. Due to the specific learning structure of ELM, the
modeling process is formulated as an iterative Least-Squares (LS) optimization
problem. Finally, the switching sequence can be reconstructed based on the
switching detection and segmented trace merging results. An example of the
data-driven modeling DC-DC converter is presented to show the effectiveness of
the developed approach.

12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
21
The categories of this paper are: adap-org