NDLI: Using particle swarm optimization in training neural network for indoor field strength prediction

Content Provider	IEEE Xplore Digital Library
Author	Vilovic, I. Burum, N. Milic, D.
Copyright Year	2009
Description	Author affiliation: University of Dubrovnik, Croatia (Vilovic, I.; Burum, N.; Milic, D.)
Abstract	This paper presents a comparison of results obtained from neural network training by backpropagation and particle swarm optimization (PSO) algorithms. The neural network model has been developed for field strength prediction in indoor environments. It has been already shown for neural networks as powerful tool in RF propagation prediction. It is very important to choose proper algorithm for training a neural network, so we compared BP training algorithms: gradient descent method and Levenberg- Marquardt algorithm with PSO algorithm. PSO algorithm has been shown as powerful method for global optimization in several applications. A floor of university building in Dubrovnik has been used as case for simulation and measurement of signal strength. The results show that the neural network weights converge faster with PSO than with standard BP algorithms.
Starting Page	275
Ending Page	278
File Size	123824
Page Count	4
File Format	PDF
ISBN	9789537044107
ISSN	13342630
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-28
Publisher Place	Croatia
Access Restriction	Subscribed
Rights Holder	Croatian Society Electronic
Subject Keyword	Training algorithm Indoor environments Optimization methods Artificial neural networks Predictive models Neural network Particle swarm optimization Backpropagation algorithm Neural networks Gradient descent algorithm Backpropagation algorithms Multilayer Perceptron Multilayer perceptrons Floors Levenberg-Marquardt algorithm Testing
Content Type	Text
Resource Type	Article

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