An indoor location system based on neural network and genetic algorithm. (2015)

• Record Type: Journal Article
• Authors: Chen, R.C.; Huang, S.W.; Lin, Y.C.; Zhao, Q.F.
• Is Part Of: International journal of sensor networks. Volume 19:Number 3/4(2015)
• Page Start: 204
• Page End: 216
• Publication Date: 2015
• Subjects: RFID -- radio frequency identification -- indoor position location -- neural networks -- GAs -- genetic algorithms -- indoor location -- received signal strength -- RSS -- tracking objects -- localisation; Sensor networks -- Periodicals; 681.2
• Languages: English
• ISSNs: 1748-1279

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.