Using fuzzy transforms for neural networks-based wireless localization in outdoor environments

Abstract

As neural network-based localization algorithms are becoming popular, there is a need to shorten the training time and the localization time for sustainability and efficiency purposes. To address such issues, the fuzzy transform (or F-transform for short) is employed here for the first time in a neural network-based localization algorithm. The F-transform is a dimensionality reduction method, which has found several applications over the last decade, but it has not been well explored in the form of a prepending layer to a neural network. In this respect, some properties (including the computational cost) of the F-transformed neural scheme are formally discussed here. The performance of the neural network-based approach with and without F-transform, and with a state-of-the-art reduction technique, i.e. the principal component analysis, is evaluated first on simulated data and then on publicly available real-world data. Different neural network architectures have been tried jointly with the above-mentioned reduction techniques. The numerical experiments show the excellent performance of the proposed fuzzy transform-based approach, which can ensure considerable savings in training time and query response time, without significant losses in accuracy.