Abstract
In this paper we apply Particle Swarm Optimization (PSO) algorithm to the training process of a Multilayer Perceptron (MLP) on the problem of localizing a mobile GSM network terminal inside a building.
The localization data includes the information about the average GSM and WiFi signals in each of the given (x,y,floor) coordinates from more than two thousand points inside a five story building.
We show that the PSO algorithm could be with success applied as an initial training algorithm for the MLP for both classification and regression problems.
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Karwowski, J., Okulewicz, M., Legierski, J. (2013). Application of Particle Swarm Optimization Algorithm to Neural Network Training Process in the Localization of the Mobile Terminal. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_13
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DOI: https://doi.org/10.1007/978-3-642-41013-0_13
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