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

被引：8
|
作者：
Jabbari, Amir [1 ]
Jedermann, Reiner [1 ]
Muthuraman, Ramanan [1 ]
Lang, Walter [1 ]
机构：
[1] Univ Bremen, IMSAS, Dept Elect Engn, D-28359 Bremen, Germany
来源：
SENSORS | 2009年 / 9卷 / 04期
关键词：
Radial basis function; back propagation; wireless sensor network; distributed Data approximation and classification; NEURAL-NETWORKS; FUSION;
D O I：
10.3390/s90403056
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
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.
引用
收藏
页码：3056 / 3077
页数：22
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