Name
Abstract | ||
|---|---|---|
Various radio systems can be used to obtain the position information in indoor environments. Due to the ubiquitous presence of WLAN networks, positioning techniques in these environments are the scope of intense research. This paper explores the properties of cascade-connected Artificial Neural Networks (ANNs) structures. Several cascade-connected ANN structures with space partitioning are compared to the single ANN multilayer feedforward structure. The benefits of using cascade-connected ANNs structures are shown and discussed in terms of the size of the environment and subspaces. The optimal cascade-connected ANN structure with space partitioning shows a 41% decrease in median error with respect to the single ANN model. |
Year | Venue | Keywords |
|---|---|---|
2009 | IDEAL | ann structure,wlan network,space partitioning,intense research,cascade-connected artificial neural networks,indoor wlan positioning,indoor environment,single ann multilayer feedforward,single ann model,cascade-connected ann structure,cascade-connected anns structure,wlan,radio,location,artificial neural network |
Field | DocType | Volume |
Space partitioning,Pattern recognition,Computer science,Linear subspace,Artificial intelligence,Cascade,Artificial neural network,Machine learning,Feed forward | Conference | 5788 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-04393-3 | 0 |
PageRank | References | Authors |
0.34 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milos Borenovic | 1 | 42 | 4.74 |
| Aleksandar Neskovic | 2 | 114 | 14.84 |
| Djuradj Budimir | 3 | 34 | 3.78 |