Optimal Artificial Neural Network design for propagation path-loss prediction using adaptive evolutionary algorithms

Sotirios P. Sotiroudis, Sotirios K. Goudos, Konstantinos A. Gotsis, Katherine Siakavara, John N. Sahalos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper we present an alternative procedure for the prediction of propagation path loss in urban environments, which is based on Artificial Neural Networks (ANN). The size of a neural network must be defined before it can be trained for any application. We apply different adaptive Differential Evolution (DE) algorithms, in order to design an optimal ANN for path loss propagation prediction. We present two different ANN design cases with two and three hidden layers respectively. The general performance of the both ANN shows their effectiveness to yield results with satisfactory accuracy in short time. The received results are compared to the respective ones yielded by the Ray-Tracing model and exhibit satisfactory accuracy.

Original languageEnglish
Title of host publication2013 7th European Conference on Antennas and Propagation, EuCAP 2013
Pages3795-3799
Number of pages5
Publication statusPublished - 2013
Event2013 7th European Conference on Antennas and Propagation, EuCAP 2013 - Gothenburg, Sweden
Duration: 8 Apr 201312 Apr 2013

Other

Other2013 7th European Conference on Antennas and Propagation, EuCAP 2013
CountrySweden
CityGothenburg
Period8/04/1312/04/13

Keywords

  • Differential Evolution
  • evolutionary algorithms
  • mobile communications
  • Neural Network
  • optimization methods
  • propagation path-loss
  • Self-adaptive Differential Evolution

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  • Cite this

    Sotiroudis, S. P., Goudos, S. K., Gotsis, K. A., Siakavara, K., & Sahalos, J. N. (2013). Optimal Artificial Neural Network design for propagation path-loss prediction using adaptive evolutionary algorithms. In 2013 7th European Conference on Antennas and Propagation, EuCAP 2013 (pp. 3795-3799). [6547020]