Title: An indoor location system based on neural network and genetic algorithm

Authors: R.C. Chen; S.W. Huang; Y.C. Lin; Q.F. Zhao

Addresses: Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan ' Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan ' Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan ' Division of Computer Science University of Aizu, Aizu-wakamatsu 965-8580, Japan

Abstract: In recent years, the position location applications have increasingly. In this paper, we will use multiple Back-Propagation neural networks with genetic algorithm (GA) for a radio frequency identification (RFID) indoor location system to provide location services named indoor location with multiple neural networks and genetic algorithms (ILMNGA). In Section 1, we collect received signal strength (RSS) information from reference points to train the neural network models. In Section 2, genetic algorithm (GA) is used to find the weight of each neural network based on the performance of each neural network. Finally, we input the RSS information of each tracking object into the model that will provide the location of tracking objects based on the RSS information. The location will be integrated using the weights produced by the GA. The experiment conducted our methodology can provide better accuracy than a single neural network.

Keywords: RFID; radio frequency identification; indoor position location; neural networks; GAs; genetic algorithms; indoor location; received signal strength; RSS; tracking objects; localisation.

DOI: 10.1504/IJSNET.2015.072863

International Journal of Sensor Networks, 2015 Vol.19 No.3/4, pp.204 - 216

Received: 21 Jan 2012
Accepted: 13 May 2012
Published online: 05 Nov 2015 *

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