| |
 Programme
Overview
Detailed
Programme
| Self-Organising
Maps (SOM) |
CHAIR: DAVID PEARSON
Time: Tuesday, March
22nd, 16h30-18h10
| SOM-1 |
| |
| Title: |
|
|
The Growing Hierarchical Self-Organizing
Feature Maps And Genetic Algorithms for Large Scale Power
System security |
| Author(s): |
M. Boudour,
A. Hellal |
| Abstract: |
This paper proposes a new methodology which
combines supervised learning, unsupervised learning and genetic
algorithm for evaluating power system dynamic security. Based
on the concept of stability margin, pre-fault power system
conditions are assigned to the output neurons on the two-dimensional
grid with the growing hierarchical self-organizing map technique
(GHSOM) via supervised ANNs which perform an estimation of
post-fault power system state. The technique estimates the
dynamic stability index that corresponds to the most critical
value of synchronizing and damping torques of multimachine
power systems. ANN-based pattern recognition is carried out
with the growing hierarchical self-organizing feature mapping
in order to provide an adaptive neural net architecture during
its unsupervised training process. Numerical tests, carried
out on a IEEE 9 bus power system are presented and discussed.
The analysis using such method provides accurate results and
improves the effectiveness of system security evaluation. |
 |
| SOM-2 |
| |
| Title: |
|
|
3D Self Organizing Convex Neural
Network Architectures |
| Author(s): |
F. Boudjemaï,
P. Biela Enberg,
J. G. Postaire |
| Abstract: |
Surface modeling and structure representation
from unorganized sample points are key problems in many applications
for whose neural networks are recently starting a gradual
breakthrough. Our purpose is the development of innovative
self organizing neural network architecture for surface modeling.
We propose an original neural architecture and algorithm inspired
from Kohonen's self organizing maps, based on dynamic neighborhood
propagation along with an adaptative learning and repulsion
process applied to a generalized mesh structure that will
lead to a topological definition of the realistic surface
given as an input. |
|
| SOM-3 |
| |
| Title: |
|
|
Novel Learning Algorithm Aiming
at Generating a Unique Units Distribution in Standard SOM |
| Author(s): |
Kirmene Marzouki,
Takeshi Yamakawa |
| Abstract: |
Self-organizing maps, SOMs, are a data visualization
technique developed to reduce the dimensions of data through
the use of self-organizing neural networks. However, one of
the limitations of Self Organizing Maps algorithm, is that
every SOM is different and finds different similarities among
the sample vectors each time the initial conditions are changed.
In this paper, we propose a modification of the SOM basic
algorithm in order to make the resulted mapping invariant
to the initial conditions. We extend the neighborhood concept
to processing units, selected in a fashionable manner, other
than those commonly selected relatively to the immediate surroundings
of the best matching unit. We also introduce a new learning
function for the newly introduced neighbors. The modified
algorithm was tested on a color classification application
and performed very well in comparison with the traditional
SOM. |
|
| SOM-4 |
| |
| Title: |
|
|
SOM-Based Estimation of Meteorological
Profiles |
| Author(s): |
T. Tambouratzis,
G. Tambouratzis |
| Abstract: |
The task of estimating the meteorological profile
of any location of interest within a specified area is undertaken.
Assuming that the meteorological profiles of a sufficient
number of representative reference locations within the area
are available, the proposed methodology is based on (a) the
organisation of the meteorological profiles of the reference
locations employing a self-organising map (SOM) and (b) the
classification of the most salient morphological characteristics
of the reference locations. The meteorological profile of
any novel location of interest is approximated by a weighted
average of the meteorological profiles represented on the
SOM for those reference locations whose morphological characteristics
most closely match the morphological characteristics of the
location of interest. The proposed methodology is evaluated
by comparing the accuracy of meteorological profile estimation
with that of existing estimation techniques as well as with
the actual meteorological profiles of the locations of interest. |
|
| SOM-5 |
| |
| Title: |
|
|
An Efficient Heuristic for the
Traveling Salesman Problem Based on a Growing SOM-like Algorithm |
| Author(s): |
C. Garcia,
J . Moreno |
| Abstract: |
A growing self-organizing map neural network,
enhanced with local search heuristic is proposed as an efficient
traveling salesman problem solver. A ring structure of processing
units with an initial small set of units is evolved in time
with a Kohonen type adaptation dynamics together with a simple
growing rule in the number of processing units. The result
is a neural network heuristic for the TSP with a computational
complexity of O(n^2), comparable to other reported SOM-like
networks. The resulting tour from the SOM network is enhanced
by the application of a simple greedy 2-Opt local search.
Experiments over a broad set of TSP instances are carried
out. The obtained experimental results show a solution accuracy
equivalent to that of the best SOM based heuristics reported
in the literature. |
|