Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor NetworksReport as inadecuate




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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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Abstract 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





Author: Amir Jabbari * , Reiner Jedermann, Ramanan Muthuraman and Walter Lang

Source: http://mdpi.com/



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