%0 Journal Article
%T Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor Networks
%A Amir Jabbari
%A Reiner Jedermann
%A Ramanan Muthuraman
%A Walter Lang
%J Sensors
%D 2009
%I MDPI AG
%R 10.3390/s90403056
%X 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 ˇ°radial basis functionˇ± (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.
%K Radial basis function
%K back propagation
%K wireless sensor network
%K distributed Data approximation and classification
%U http://www.mdpi.com/1424-8220/9/4/3056