Open AccessProceedings Article
A comparison of MLP and RBF neural network architectures for location determination in indoor environments
Ivan Vilovic,Niksa Burum +1 more
- pp 3496-3499
TLDR
Two different neural network architectures are investigated for enough accurate position determination of a mobile device in the complex indoor environment using multilayer perceptron and radial basis function neural networks.Abstract:
In this paper two different neural network architectures are investigated for enough accurate position determination of a mobile device in the complex indoor environment The investigation includes multilayer perceptron (MLP) and radial basis function (RBF) neural networks It has been already shown for neural networks as powerful tool in RF propagation prediction The research is based on dependence of the received signal with distance The neural networks are trained by three training algorithms: scaled conjugate, resilient backpropagation and Levenberg-Marquardit with Bayesian regularization The obtained results for position prediction show error that is less than 025 mread more
Citations
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Journal ArticleDOI
A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation.
TL;DR: The proposed MLP-MLP-RBF network is less computationally intensive than the classical DPD algorithm and is therefore an attractive choice for real-time applications.
Proceedings ArticleDOI
An Algorithm Based on Bayes Inference And K-nearest Neighbor For 3D WLAN Indoor Positioning
Hitalo Nascimento,Emanuel Bezerra Rodrigues,Francisco R. P. Cavalcanti,Antonio Regilane Lima Paiva +3 more
TL;DR: A hybrid algorithm based on Bayesian inference and K-Nearest Neighbor to estimate the threedimensional indoor positioning implemented from a fingerprint technique has lower variability than other algorithms, with deviation in relation to the mean reaches of 37.62%.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Proceedings ArticleDOI
RADAR: an in-building RF-based user location and tracking system
TL;DR: RADAR is presented, a radio-frequency (RF)-based system for locating and tracking users inside buildings that combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications.
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Location systems for ubiquitous computing
TL;DR: This survey and taxonomy of location systems for mobile-computing applications describes a spectrum of current products and explores the latest in the field to help developers of location-aware applications better evaluate their options when choosing a location-sensing system.
Proceedings ArticleDOI
Location determination of a mobile device using IEEE 802.11b access point signals
TL;DR: This paper exploits the fact that the strength of the signals that a device will receive from different access points will vary with location, and builds a database of signal strength information for various locations, and uses this information to determine which location a given test data comes from.
Location-aware computing: a neural network model for determining location in wireless LANs
TL;DR: The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics.