Applied Research on Discriminative-Adaptive Neural Network Algorithm in Indoor Positioning System

Author

Du, Youfu ; Zhao, Ming ; Hu, Yanghong

Author_Institution

Sch. of Comput. Sci., Yangtze Univ., Jingzhou, China

fYear

2011

Firstpage

333

Lastpage

336

Abstract

In this paper, a three-layer Discriminative-Adaptive Neural Network algorithm (DANN) is proposed in indoor Positioning System. By using Multivariate Discriminant Analysis, a weight matrix could be acquired, then it could input these weighting signals into the Neural Network for training, and it could get a minimum mean square error, finally, we could get the Minimum error positioning results. Experimental results show that using DANN algorithm can obviously improve the indoor positioning precision and speed.

Keywords

Global Positioning System; least mean squares methods; matrix algebra; neural nets; telecommunication computing; MMSE; indoor positioning system; minimum mean square error; multivariate discriminant analysis; three-layer discriminative-adaptive neural network algorithm; weight matrix; weighting signals; Algorithm design and analysis; Educational institutions; Fingerprint recognition; Instruments; Radar tracking; Training; Wireless LAN; Discriminant Analysis; neural networkt; positioning system; weight matrix;

fLanguage

English

Publisher

ieee

Conference_Titel

Genetic and Evolutionary Computing (ICGEC), 2011 Fifth International Conference on

Conference_Location

Xiamen

Print_ISBN

978-1-4577-0817-6

Electronic_ISBN

978-0-7695-4449-6

Type

conf

DOI

10.1109/ICGEC.2011.83

Filename

6042794