Multilayer feedforward networks are universal approximators
Neural Networks
Machine Learning
Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications
Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications
Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
802.11 Wireless Networks: The Definitive Guide
802.11 Wireless Networks: The Definitive Guide
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
The book of wireless, 2nd edition
The book of wireless, 2nd edition
Real-Time Robot Localization in Indoor Environments Using Structural Information
LARS '08 Proceedings of the 2008 IEEE Latin American Robotic Symposium
The 2005 DARPA Grand Challenge: The Great Robot Race
The 2005 DARPA Grand Challenge: The Great Robot Race
Personal Networks: Wireless Networking for Personal Devices
Personal Networks: Wireless Networking for Personal Devices
Vision-Based Autonomous Navigation System Using ANN and FSM Control
LARS '10 Proceedings of the 2010 Latin American Robotics Symposium and Intelligent Robotics Meeting
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.