title: Application of neurocomputing for data approximation and classification in wireless sensor networks.
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.
authors.affiliation: Department of Electrical Engineering, Institute of Micro sensors, Actuators and Systems (IMSAS), University of Bremen, NW1 Building, D-28359 Bremen, Germany.
authors.forename: Amir
authors.initials: A
authors.lastname: Jabbari
authors.forename: Reiner
authors.initials: R
authors.lastname: Jedermann
authors.forename: Ramanan
authors.initials: R
authors.lastname: Muthuraman
authors.forename: Walter
authors.initials: W
authors.lastname: Lang