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Open AccessArticle

Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor Networks

Department of Electrical Engineering, Institute of Micro sensors, Actuators and Systems (IMSAS), University of Bremen, NW1 Building, D-28359 Bremen, Germany
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Sensors 2009, 9(4), 3056-3077; https://doi.org/10.3390/s90403056
Received: 3 April 2009 / Revised: 21 April 2009 / Accepted: 24 April 2009 / Published: 24 April 2009
(This article belongs to the Special Issue Neural Networks and Sensors)
A new application of neurocomputing for data approximation and classification is introduced to process data in a wireless sensor network. For this purpose, a simplified dynamic sliding backpropagation algorithm is implemented on a wireless sensor network for transportation applications. It is able to approximate temperature and humidity in sensor nodes. In addition, two architectures of “radial basis function” (RBF) classifiers are introduced with probabilistic features for data classification in sensor nodes. The applied approximation and classification algorithms could be used in similar applications for data processing in embedded systems. View Full-Text
Keywords: Radial basis function; back propagation; wireless sensor network; distributed Data approximation and classification Radial basis function; back propagation; wireless sensor network; distributed Data approximation and classification
MDPI and ACS Style

Jabbari, A.; Jedermann, R.; Muthuraman, R.; Lang, W. Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor Networks. Sensors 2009, 9, 3056-3077.

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