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

Sensors

Amir Jabbari¹  Reiner Jedermann²  Ramanan Muthuraman² 

[1] id="af1-sensors-09-03056">Department of Electrical Engineering, Institute of Micro sensors, Actuators and Systems (IMSAS), University of Bremen, NW1 Building, D-28359 Bremen, Germa

关键词: Radial basis function;    back propagation;    wireless sensor network;    distributed Data approximation and classification;   

DOI  :  10.3390/s90403056

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland