Izvorni znanstveni članak
SIGNAL STRENGTH PREDICTION IN INDOOR ENVIRONMENTS BASED ON NEURAL NETWORK MODEL AND PARTICLE SWARM OPTIMIZATION
Ivan Vilović
orcid.org/0000-0001-7578-758X
; Elektrotehničko računarski odjel Sveučilište u Dubrovniku
Robert Nađ
orcid.org/0000-0001-8011-1429
; Sveučilište u Zagrebu
Nikša Burum
; Elektrotehničko računarski odjel Sveučilište u Dubrovniku
Sažetak
This paper deals with an indoor propagation problem where it is difficult to rigorously obtain the field strength
distribution. We have developed a propagation model based on a neural network, which has advantages of
deterministic (high accuracy) and empirical (short computation time) approaches. The neural network architecture,
based on the multilayer perception, is used to absorb the knowledge about the given environment through training
based on measurements. Such network then becomes capable to predict signal strength that includes absorption
and reflection effects without additional computation and measurement efforts. The neural network model is used
as a cost function in the optimization of the base station location. As optimization algorithm we have applied the
particle swarm optimization (PSO) algorithm, i.e. a global optimization routine based on the movement of particles
and their intelligence. Appropriate PSO parameters are discussed in the paper, and the results of PSO are compared
with results obtained with two standard algorithms such as simplex optimization method and Powell’s conjugate
direction method.
Ključne riječi
Indoor propagation; Neural network; Particle Swarm Optimization
Hrčak ID:
42136
URI
Datum izdavanja:
27.10.2009.
Posjeta: 1.378 *