Application of neurocomputing for data approximation and classification in wireless sensor networks

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 t...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2009-04, Vol.9 (4), p.3056-3077
Main Authors: Jabbari, Amir, Jedermann, Reiner, Muthuraman, Ramanan, Lang, Walter
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Approximation
Back propagation
Classification
Data processing
Decision making
distributed Data approximation and classification
Embedded systems
Humidity
Neural networks
Radial basis function
Sensors
wireless sensor network
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s90403056