182,862 research outputs found
Winner-Relaxing Self-Organizing Maps
A new family of self-organizing maps, the Winner-Relaxing Kohonen Algorithm,
is introduced as a generalization of a variant given by Kohonen in 1991. The
magnification behaviour is calculated analytically. For the original variant a
magnification exponent of 4/7 is derived; the generalized version allows to
steer the magnification in the wide range from exponent 1/2 to 1 in the
one-dimensional case, thus provides optimal mapping in the sense of information
theory. The Winner Relaxing Algorithm requires minimal extra computations per
learning step and is conveniently easy to implement.Comment: 14 pages (6 figs included). To appear in Neural Computatio
Fault prediction in aircraft engines using Self-Organizing Maps
Aircraft engines are designed to be used during several tens of years. Their
maintenance is a challenging and costly task, for obvious security reasons. The
goal is to ensure a proper operation of the engines, in all conditions, with a
zero probability of failure, while taking into account aging. The fact that the
same engine is sometimes used on several aircrafts has to be taken into account
too. The maintenance can be improved if an efficient procedure for the
prediction of failures is implemented. The primary source of information on the
health of the engines comes from measurement during flights. Several variables
such as the core speed, the oil pressure and quantity, the fan speed, etc. are
measured, together with environmental variables such as the outside
temperature, altitude, aircraft speed, etc. In this paper, we describe the
design of a procedure aiming at visualizing successive data measured on
aircraft engines. The data are multi-dimensional measurements on the engines,
which are projected on a self-organizing map in order to allow us to follow the
trajectories of these data over time. The trajectories consist in a succession
of points on the map, each of them corresponding to the two-dimensional
projection of the multi-dimensional vector of engine measurements. Analyzing
the trajectories aims at visualizing any deviation from a normal behavior,
making it possible to anticipate an operation failure.Comment: Communication pr\'esent\'ee au 7th International Workshop WSOM 09, St
Augustine, Floride, USA, June 200
Self-Organizing Maps and Parton Distributions Functions
We present a new method to extract parton distribution functions from high
energy experimental data based on a specific type of neural networks, the
Self-Organizing Maps. We illustrate the features of our new procedure that are
particularly useful for an anaysis directed at extracting generalized parton
distributions from data. We show quantitative results of our initial analysis
of the parton distribution functions from inclusive deep inelastic scattering.Comment: 8 pages, 4 figures, to appear in the proceedings of "Workshop on
Exclusive Reactions at High Momentum Transfer (IV)", Jefferson Lab, May 18th
-21st, 201
Analysis of Data Clusters Obtained by Self-Organizing Methods
The self-organizing methods were used for the investigation of financial
market. As an example we consider data time-series of Dow Jones index for the
years 2002-2003 (R. Mantegna, cond-mat/9802256). In order to reveal new
structures in stock market behavior of the companies drawing up Dow Jones index
we apply SOM (Self-Organizing Maps) and GMDH (Group Method of Data Handling)
algorithms. Using SOM techniques we obtain SOM-maps that establish a new
relationship in market structure. Analysis of the obtained clusters was made by
GMDH.Comment: 10 pages, 4 figure
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